scirs2_vision/
visual_slam.rs

1//! Advanced Visual SLAM Framework
2//!
3//! This module provides sophisticated Visual Simultaneous Localization and Mapping capabilities including:
4//! - Real-time camera pose estimation
5//! - 3D map reconstruction and optimization
6//! - Loop closure detection and correction
7//! - Dense and sparse mapping approaches
8//! - Multi-scale and multi-sensor fusion
9//! - Semantic SLAM with object-level understanding
10
11#![allow(dead_code)]
12#![allow(missing_docs)]
13
14use crate::error::{Result, VisionError};
15use crate::scene_understanding::SceneAnalysisResult;
16use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView3};
17use std::collections::HashMap;
18
19/// Advanced-advanced Visual SLAM system with multi-modal capabilities
20pub struct VisualSLAMSystem {
21    /// Camera pose estimator
22    pose_estimator: CameraPoseEstimator,
23    /// 3D map builder and manager
24    map_builder: Map3DBuilder,
25    /// Loop closure detector
26    loop_detector: LoopClosureDetector,
27    /// Bundle adjustment optimizer
28    bundle_adjuster: BundleAdjustmentOptimizer,
29    /// Feature tracker and matcher
30    feature_tracker: AdvancedFeatureTracker,
31    /// Semantic map builder
32    semantic_mapper: SemanticMapper,
33    /// Multi-sensor fusion module
34    sensor_fusion: MultiSensorFusion,
35    /// SLAM knowledge base
36    knowledge_base: SLAMKnowledgeBase,
37}
38
39/// Advanced camera pose estimation with uncertainty quantification
40#[derive(Debug, Clone)]
41pub struct CameraPoseEstimator {
42    /// Pose estimation method
43    estimation_method: PoseEstimationMethod,
44    /// Motion model for prediction
45    motion_model: MotionModel,
46    /// Uncertainty propagation parameters
47    uncertainty_params: UncertaintyParams,
48    /// Robust estimation parameters
49    robust_params: RobustEstimationParams,
50}
51
52/// 3D map building and management
53#[derive(Debug, Clone)]
54pub struct Map3DBuilder {
55    /// Map representation type
56    map_representation: MapRepresentationType,
57    /// Keyframe selection strategy
58    keyframe_strategy: KeyframeStrategy,
59    /// Map optimization parameters
60    optimization_params: MapOptimizationParams,
61    /// Map maintenance settings
62    maintenance_params: MapMaintenanceParams,
63}
64
65/// Loop closure detection for global consistency
66#[derive(Debug, Clone)]
67pub struct LoopClosureDetector {
68    /// Detection method
69    detection_method: LoopDetectionMethod,
70    /// Visual vocabulary parameters
71    vocabulary_params: VisualVocabularyParams,
72    /// Geometric verification settings
73    geometric_verification: GeometricVerificationParams,
74    /// Loop closure threshold
75    closure_threshold: f32,
76}
77
78/// Bundle adjustment for map optimization
79#[derive(Debug, Clone)]
80pub struct BundleAdjustmentOptimizer {
81    /// Optimization method
82    optimization_method: String,
83    /// Convergence criteria
84    convergence_criteria: ConvergenceCriteria,
85    /// Robust cost functions
86    robust_cost_functions: Vec<RobustCostFunction>,
87    /// Optimization windows
88    optimization_windows: OptimizationWindows,
89}
90
91/// Advanced feature tracking and matching
92#[derive(Debug, Clone)]
93pub struct AdvancedFeatureTracker {
94    /// Feature detection methods
95    feature_detectors: Vec<String>,
96    /// Feature matching strategies
97    matching_strategies: Vec<MatchingStrategy>,
98    /// Temporal tracking parameters
99    temporal_tracking: TemporalTrackingParams,
100    /// Multi-scale tracking
101    multiscale_params: MultiscaleParams,
102}
103
104/// Semantic mapping for object-level understanding
105#[derive(Debug, Clone)]
106pub struct SemanticMapper {
107    /// Object detection integration
108    object_detection: ObjectDetectionIntegration,
109    /// Semantic segmentation integration
110    segmentation_integration: SegmentationIntegration,
111    /// Semantic map representation
112    semantic_representation: SemanticMapRepresentation,
113    /// Object-level SLAM parameters
114    object_slam_params: ObjectSLAMParams,
115}
116
117/// Multi-sensor fusion for robust SLAM
118#[derive(Debug, Clone)]
119pub struct MultiSensorFusion {
120    /// Supported sensor types
121    sensor_types: Vec<SensorType>,
122    /// Fusion strategies
123    fusion_strategies: Vec<FusionStrategy>,
124    /// Sensor calibration parameters
125    calibration_params: SensorCalibrationParams,
126    /// Temporal synchronization
127    synchronization_params: TemporalSynchronizationParams,
128}
129
130/// SLAM knowledge base for learning and adaptation
131#[derive(Debug, Clone)]
132pub struct SLAMKnowledgeBase {
133    /// Environment models
134    environment_models: Vec<EnvironmentModel>,
135    /// Motion patterns
136    motion_patterns: Vec<MotionPattern>,
137    /// Failure modes and recovery
138    failure_recovery: FailureRecoveryParams,
139    /// Performance metrics
140    performance_metrics: PerformanceMetrics,
141}
142
143/// Comprehensive SLAM result with trajectory and map
144#[derive(Debug, Clone)]
145pub struct SLAMResult {
146    /// Camera trajectory
147    pub trajectory: CameraTrajectory,
148    /// 3D map of the environment
149    pub map_3d: Map3D,
150    /// Semantic map with object annotations
151    pub semantic_map: SemanticMap,
152    /// Loop closures detected
153    pub loop_closures: Vec<LoopClosure>,
154    /// Pose uncertainty over time
155    pub pose_uncertainty: Vec<PoseUncertainty>,
156    /// Map quality metrics
157    pub map_quality: MapQuality,
158    /// SLAM performance statistics
159    pub performance_stats: SLAMPerformanceStats,
160}
161
162/// Camera trajectory representation
163#[derive(Debug, Clone)]
164pub struct CameraTrajectory {
165    /// Timestamps
166    pub timestamps: Vec<f64>,
167    /// Camera poses (rotation + translation)
168    pub poses: Vec<CameraPose>,
169    /// Pose covariances
170    pub covariances: Vec<Array2<f64>>,
171    /// Trajectory smoothness metrics
172    pub smoothness_metrics: TrajectoryMetrics,
173}
174
175/// Individual camera pose
176#[derive(Debug, Clone)]
177pub struct CameraPose {
178    /// Position (x, y, z)
179    pub position: Array1<f64>,
180    /// Rotation quaternion (w, x, y, z)
181    pub rotation: Array1<f64>,
182    /// Pose confidence
183    pub confidence: f32,
184    /// Frame ID
185    pub frame_id: usize,
186}
187
188/// 3D map representation
189#[derive(Debug, Clone)]
190pub struct Map3D {
191    /// 3D landmark points
192    pub landmarks: Vec<Landmark3D>,
193    /// Map structure (graph connectivity)
194    pub structure: MapStructure,
195    /// Map bounds
196    pub bounds: MapBounds,
197    /// Map resolution
198    pub resolution: f32,
199    /// Map confidence distribution
200    pub confidence_map: Array3<f32>,
201}
202
203/// Individual 3D landmark
204#[derive(Debug, Clone)]
205pub struct Landmark3D {
206    /// 3D position
207    pub position: Array1<f64>,
208    /// Landmark descriptor
209    pub descriptor: Array1<f32>,
210    /// Observation count
211    pub observation_count: usize,
212    /// Uncertainty estimate
213    pub uncertainty: Array2<f64>,
214    /// Landmark ID
215    pub landmark_id: usize,
216    /// Associated semantic label
217    pub semantic_label: Option<String>,
218}
219
220/// Semantic map with object-level information
221#[derive(Debug, Clone)]
222pub struct SemanticMap {
223    /// Semantic objects in the environment
224    pub semantic_objects: Vec<SemanticObject>,
225    /// Object relationships
226    pub object_relationships: Vec<ObjectRelationship>,
227    /// Scene understanding results
228    pub scene_understanding: Vec<SceneSegment>,
229    /// Semantic consistency metrics
230    pub consistency_metrics: SemanticConsistencyMetrics,
231}
232
233/// Semantic object in the map
234#[derive(Debug, Clone)]
235pub struct SemanticObject {
236    /// Object class
237    pub object_class: String,
238    /// 3D bounding box or mesh
239    pub geometry: ObjectGeometry,
240    /// Object confidence
241    pub confidence: f32,
242    /// Associated observations
243    pub observations: Vec<ObjectObservation>,
244    /// Object attributes
245    pub attributes: HashMap<String, f32>,
246}
247
248/// Loop closure information
249#[derive(Debug, Clone)]
250pub struct LoopClosure {
251    /// Query frame ID
252    pub query_frame: usize,
253    /// Match frame ID
254    pub match_frame: usize,
255    /// Relative transformation
256    pub relative_transform: Array2<f64>,
257    /// Closure confidence
258    pub confidence: f32,
259    /// Number of matched features
260    pub matched_features: usize,
261    /// Geometric verification score
262    pub geometric_score: f32,
263}
264
265/// Pose uncertainty quantification
266#[derive(Debug, Clone)]
267pub struct PoseUncertainty {
268    /// Frame ID
269    pub frame_id: usize,
270    /// Position uncertainty (3x3 covariance)
271    pub position_uncertainty: Array2<f64>,
272    /// Rotation uncertainty (3x3 covariance)
273    pub rotation_uncertainty: Array2<f64>,
274    /// Overall confidence
275    pub overall_confidence: f32,
276}
277
278/// Map quality assessment
279#[derive(Debug, Clone)]
280pub struct MapQuality {
281    /// Overall map quality score
282    pub overall_score: f32,
283    /// Local consistency scores
284    pub local_consistency: Vec<f32>,
285    /// Global consistency score
286    pub global_consistency: f32,
287    /// Feature density metrics
288    pub feature_density: FeatureDensityMetrics,
289    /// Reconstruction accuracy
290    pub reconstruction_accuracy: f32,
291}
292
293/// SLAM performance statistics
294#[derive(Debug, Clone)]
295pub struct SLAMPerformanceStats {
296    /// Processing time per frame
297    pub processing_times: Vec<f64>,
298    /// Memory usage over time
299    pub memory_usage: Vec<usize>,
300    /// Tracking success rate
301    pub tracking_success_rate: f32,
302    /// Loop closure detection rate
303    pub loop_closure_rate: f32,
304    /// Map update frequency
305    pub map_update_frequency: f32,
306    /// Overall system robustness
307    pub robustness_score: f32,
308}
309
310// Supporting types for Visual SLAM
311#[derive(Debug, Clone)]
312pub enum PoseEstimationMethod {
313    PerspectiveNPoint,
314    EssentialMatrix,
315    VisualInertialOdometry,
316    DirectMethods,
317    HybridApproach,
318}
319
320#[derive(Debug, Clone)]
321pub struct MotionModel {
322    pub model_type: String,
323    pub prediction_noise: Array2<f64>,
324    pub process_noise: Array2<f64>,
325    pub motion_constraints: Vec<MotionConstraint>,
326}
327
328#[derive(Debug, Clone)]
329pub struct MotionConstraint {
330    pub constraint_type: String,
331    pub min_value: f64,
332    pub max_value: f64,
333    pub weight: f32,
334}
335
336#[derive(Debug, Clone)]
337pub struct UncertaintyParams {
338    pub propagation_method: String,
339    pub uncertainty_inflation: f32,
340    pub minimum_uncertainty: f32,
341    pub maximum_uncertainty: f32,
342}
343
344#[derive(Debug, Clone)]
345pub struct RobustEstimationParams {
346    pub outlier_threshold: f32,
347    pub max_iterations: usize,
348    pub convergence_threshold: f32,
349    pub robust_kernel: String,
350}
351
352#[derive(Debug, Clone)]
353pub enum MapRepresentationType {
354    PointCloud,
355    Voxel,
356    SurfaceReconstruction,
357    HybridRepresentation,
358    NeuralImplicit,
359}
360
361#[derive(Debug, Clone)]
362pub struct KeyframeStrategy {
363    pub selection_criteria: Vec<KeyframeCriterion>,
364    pub min_keyframe_distance: f32,
365    pub max_keyframe_interval: f32,
366    pub quality_threshold: f32,
367}
368
369#[derive(Debug, Clone)]
370pub enum KeyframeCriterion {
371    TranslationDistance,
372    RotationAngle,
373    FeatureOverlap,
374    UncertaintyIncrease,
375    TemporalGap,
376}
377
378#[derive(Debug, Clone)]
379pub struct MapOptimizationParams {
380    pub optimization_frequency: f32,
381    pub optimization_window_size: usize,
382    pub convergence_criteria: ConvergenceCriteria,
383    pub regularization_weights: HashMap<String, f32>,
384}
385
386#[derive(Debug, Clone)]
387pub struct MapMaintenanceParams {
388    pub landmark_culling_threshold: f32,
389    pub map_size_limit: usize,
390    pub observation_count_threshold: usize,
391    pub uncertainty_threshold: f32,
392}
393
394#[derive(Debug, Clone)]
395pub enum LoopDetectionMethod {
396    BagOfWords,
397    NetVLAD,
398    SuperGlue,
399    GeometricHashing,
400    HybridApproach,
401}
402
403#[derive(Debug, Clone)]
404pub struct VisualVocabularyParams {
405    pub vocabulary_size: usize,
406    pub descriptor_type: String,
407    pub clustering_method: String,
408    pub update_frequency: f32,
409}
410
411#[derive(Debug, Clone)]
412pub struct GeometricVerificationParams {
413    pub verification_method: String,
414    pub inlier_threshold: f32,
415    pub min_inliers: usize,
416    pub max_iterations: usize,
417}
418
419#[derive(Debug, Clone)]
420pub struct ConvergenceCriteria {
421    pub max_iterations: usize,
422    pub cost_change_threshold: f64,
423    pub parameter_change_threshold: f64,
424    pub gradient_threshold: f64,
425}
426
427#[derive(Debug, Clone)]
428pub struct RobustCostFunction {
429    pub function_type: String,
430    pub scale_parameter: f64,
431    pub applicable_residuals: Vec<String>,
432}
433
434#[derive(Debug, Clone)]
435pub struct OptimizationWindows {
436    pub local_window_size: usize,
437    pub global_optimization_frequency: usize,
438    pub sliding_window_enabled: bool,
439}
440
441#[derive(Debug, Clone)]
442pub struct MatchingStrategy {
443    pub strategy_name: String,
444    pub feature_type: String,
445    pub matching_threshold: f32,
446    pub ratio_test_threshold: f32,
447}
448
449#[derive(Debug, Clone)]
450pub struct TemporalTrackingParams {
451    pub tracking_window: usize,
452    pub prediction_enabled: bool,
453    pub track_validation: bool,
454    pub maximum_track_age: usize,
455}
456
457#[derive(Debug, Clone)]
458pub struct MultiscaleParams {
459    pub scale_levels: Vec<f32>,
460    pub feature_distribution: String,
461    pub scale_invariance: bool,
462}
463
464#[derive(Debug, Clone)]
465pub struct ObjectDetectionIntegration {
466    pub detection_frequency: f32,
467    pub confidence_threshold: f32,
468    pub object_tracking_enabled: bool,
469    pub object_map_integration: bool,
470}
471
472#[derive(Debug, Clone)]
473pub struct SegmentationIntegration {
474    pub segmentation_method: String,
475    pub semantic_consistency_check: bool,
476    pub temporal_coherence: bool,
477}
478
479#[derive(Debug, Clone)]
480pub enum SemanticMapRepresentation {
481    ObjectMap,
482    SegmentationMap,
483    HybridSemanticMap,
484    PanopticMap,
485}
486
487#[derive(Debug, Clone)]
488pub struct ObjectSLAMParams {
489    pub object_initialization_threshold: f32,
490    pub object_tracking_parameters: HashMap<String, f32>,
491    pub object_map_optimization: bool,
492}
493
494#[derive(Debug, Clone)]
495pub enum SensorType {
496    MonocularCamera,
497    StereoCamera,
498    RGBDCamera,
499    IMU,
500    LiDAR,
501    GPS,
502    Odometry,
503}
504
505#[derive(Debug, Clone)]
506pub struct FusionStrategy {
507    pub strategy_name: String,
508    pub sensor_weights: HashMap<SensorType, f32>,
509    pub fusion_frequency: f32,
510    pub temporal_alignment: bool,
511}
512
513#[derive(Debug, Clone)]
514pub struct SensorCalibrationParams {
515    pub intrinsic_calibration: HashMap<SensorType, Array2<f64>>,
516    pub extrinsic_calibration: HashMap<String, Array2<f64>>,
517    pub online_calibration: bool,
518}
519
520#[derive(Debug, Clone)]
521pub struct TemporalSynchronizationParams {
522    pub synchronization_method: String,
523    pub maximum_time_offset: f64,
524    pub interpolation_method: String,
525}
526
527#[derive(Debug, Clone)]
528pub struct EnvironmentModel {
529    pub environment_type: String,
530    pub typical_features: Vec<String>,
531    pub motion_characteristics: MotionCharacteristics,
532    pub adaptation_parameters: HashMap<String, f32>,
533}
534
535#[derive(Debug, Clone)]
536pub struct MotionCharacteristics {
537    pub typical_velocities: Array1<f32>,
538    pub acceleration_patterns: Array1<f32>,
539    pub motion_smoothness: f32,
540}
541
542#[derive(Debug, Clone)]
543pub struct MotionPattern {
544    pub pattern_name: String,
545    pub velocity_profile: Array1<f32>,
546    pub acceleration_profile: Array1<f32>,
547    pub occurrence_frequency: f32,
548}
549
550#[derive(Debug, Clone)]
551pub struct FailureRecoveryParams {
552    pub failure_detection_threshold: f32,
553    pub recovery_strategies: Vec<RecoveryStrategy>,
554    pub re_initialization_triggers: Vec<String>,
555}
556
557#[derive(Debug, Clone)]
558pub struct RecoveryStrategy {
559    pub strategy_name: String,
560    pub applicable_failures: Vec<String>,
561    pub recovery_success_rate: f32,
562}
563
564#[derive(Debug, Clone)]
565pub struct PerformanceMetrics {
566    pub tracking_accuracy_metrics: HashMap<String, f32>,
567    pub mapping_quality_metrics: HashMap<String, f32>,
568    pub computational_efficiency: ComputationalEfficiency,
569}
570
571#[derive(Debug, Clone)]
572pub struct ComputationalEfficiency {
573    pub average_processing_time: f64,
574    pub memory_footprint: usize,
575    pub cpu_utilization: f32,
576    pub gpu_utilization: f32,
577}
578
579#[derive(Debug, Clone)]
580pub struct TrajectoryMetrics {
581    pub smoothness_score: f32,
582    pub velocity_consistency: f32,
583    pub acceleration_smoothness: f32,
584    pub overall_quality: f32,
585}
586
587#[derive(Debug, Clone)]
588pub struct MapStructure {
589    pub connectivity_graph: ConnectivityGraph,
590    pub landmark_associations: HashMap<usize, Vec<usize>>,
591    pub keyframe_graph: KeyframeGraph,
592}
593
594#[derive(Debug, Clone)]
595pub struct ConnectivityGraph {
596    pub nodes: Vec<GraphNode>,
597    pub edges: Vec<GraphEdge>,
598    pub adjacency_matrix: Array2<bool>,
599}
600
601#[derive(Debug, Clone)]
602pub struct GraphNode {
603    pub node_id: usize,
604    pub node_type: String,
605    pub position: Array1<f64>,
606    pub properties: HashMap<String, f32>,
607}
608
609#[derive(Debug, Clone)]
610pub struct GraphEdge {
611    pub source: usize,
612    pub target: usize,
613    pub weight: f32,
614    pub edge_type: String,
615}
616
617#[derive(Debug, Clone)]
618pub struct KeyframeGraph {
619    pub keyframes: Vec<Keyframe>,
620    pub connections: Vec<KeyframeConnection>,
621    pub spanning_tree: Vec<(usize, usize)>,
622}
623
624#[derive(Debug, Clone)]
625pub struct Keyframe {
626    pub frame_id: usize,
627    pub pose: CameraPose,
628    pub features: Vec<Feature2D>,
629    pub image_data: Option<Array3<f32>>,
630}
631
632#[derive(Debug, Clone)]
633pub struct Feature2D {
634    pub position: Array1<f32>,
635    pub descriptor: Array1<f32>,
636    pub landmark_id: Option<usize>,
637    pub confidence: f32,
638}
639
640#[derive(Debug, Clone)]
641pub struct KeyframeConnection {
642    pub keyframe1: usize,
643    pub keyframe2: usize,
644    pub relative_pose: Array2<f64>,
645    pub covariance: Array2<f64>,
646    pub connection_strength: f32,
647}
648
649#[derive(Debug, Clone)]
650pub struct MapBounds {
651    pub min_bounds: Array1<f64>,
652    pub max_bounds: Array1<f64>,
653    pub center: Array1<f64>,
654    pub extent: Array1<f64>,
655}
656
657#[derive(Debug, Clone)]
658pub struct ObjectRelationship {
659    pub object1_id: usize,
660    pub object2_id: usize,
661    pub relationship_type: String,
662    pub confidence: f32,
663    pub spatial_constraint: SpatialConstraint,
664}
665
666#[derive(Debug, Clone)]
667pub struct SpatialConstraint {
668    pub constraint_type: String,
669    pub parameters: HashMap<String, f32>,
670    pub tolerance: f32,
671}
672
673#[derive(Debug, Clone)]
674pub struct SceneSegment {
675    pub segment_id: usize,
676    pub semantic_label: String,
677    pub confidence: f32,
678    pub spatial_extent: Array2<f64>,
679    pub associated_objects: Vec<usize>,
680}
681
682#[derive(Debug, Clone)]
683pub struct SemanticConsistencyMetrics {
684    pub temporal_consistency: f32,
685    pub spatial_consistency: f32,
686    pub label_stability: f32,
687    pub overall_consistency: f32,
688}
689
690#[derive(Debug, Clone)]
691pub enum ObjectGeometry {
692    BoundingBox3D(Array2<f64>),
693    ConvexHull(Vec<Array1<f64>>),
694    TriangularMesh(TriangleMesh),
695    PointCloud(Array2<f64>),
696}
697
698#[derive(Debug, Clone)]
699pub struct TriangleMesh {
700    pub vertices: Array2<f64>,
701    pub faces: Array2<usize>,
702    pub normals: Array2<f64>,
703    pub texture_coordinates: Option<Array2<f32>>,
704}
705
706/// Object observation data for semantic SLAM
707///
708/// Represents a detected object instance with its spatial and temporal
709/// properties for building semantic maps.
710#[derive(Debug, Clone)]
711pub struct ObjectObservation {
712    /// Frame ID where this observation occurred
713    pub frame_id: usize,
714    /// Confidence score for object detection
715    pub detection_confidence: f32,
716    /// 2D bounding box coordinates [x, y, width, height]
717    pub bounding_box_2d: Array1<f32>,
718    /// Feature point indices that match this object
719    pub feature_matches: Vec<usize>,
720}
721
722/// Metrics for analyzing feature point density and distribution
723///
724/// Provides quality metrics for feature detection and tracking
725/// performance across the image space.
726#[derive(Debug, Clone)]
727pub struct FeatureDensityMetrics {
728    /// Average feature density across the image
729    pub average_density: f32,
730    /// Distribution of feature density by image region
731    pub density_distribution: Array1<f32>,
732    /// Uniformity measure of feature coverage (0.0 to 1.0)
733    pub coverage_uniformity: f32,
734    /// Quality scores for individual features
735    pub feature_quality_scores: Vec<f32>,
736}
737
738impl Default for VisualSLAMSystem {
739    fn default() -> Self {
740        Self::new()
741    }
742}
743
744impl VisualSLAMSystem {
745    /// Create a new advanced Visual SLAM system
746    pub fn new() -> Self {
747        Self {
748            pose_estimator: CameraPoseEstimator::new(),
749            map_builder: Map3DBuilder::new(),
750            loop_detector: LoopClosureDetector::new(),
751            bundle_adjuster: BundleAdjustmentOptimizer::new(),
752            feature_tracker: AdvancedFeatureTracker::new(),
753            semantic_mapper: SemanticMapper::new(),
754            sensor_fusion: MultiSensorFusion::new(),
755            knowledge_base: SLAMKnowledgeBase::new(),
756        }
757    }
758
759    /// Process a single frame for SLAM
760    pub fn process_frame(
761        &mut self,
762        frame: &ArrayView3<f32>,
763        timestamp: f64,
764        scene_analysis: Option<&SceneAnalysisResult>,
765    ) -> Result<SLAMResult> {
766        // Extract and track features
767        let features = self.feature_tracker.extract_and_track_features(frame)?;
768
769        // Estimate camera pose
770        let pose = self.pose_estimator.estimate_pose(&features, timestamp)?;
771
772        // Update 3D map
773        let map_update = self.map_builder.update_map(&features, &pose)?;
774
775        // Check for loop closures
776        let loop_closures = self.loop_detector.detect_closures(&features, &pose)?;
777
778        // Perform bundle adjustment if needed
779        if !loop_closures.is_empty() {
780            self.bundle_adjuster
781                .optimize_map(&map_update, &loop_closures)?;
782        }
783
784        // Update semantic map if scene _analysis is available
785        let semantic_map = if let Some(scene) = scene_analysis {
786            self.semantic_mapper.update_semantic_map(scene, &pose)?
787        } else {
788            SemanticMap {
789                semantic_objects: Vec::new(),
790                object_relationships: Vec::new(),
791                scene_understanding: Vec::new(),
792                consistency_metrics: SemanticConsistencyMetrics {
793                    temporal_consistency: 0.0,
794                    spatial_consistency: 0.0,
795                    label_stability: 0.0,
796                    overall_consistency: 0.0,
797                },
798            }
799        };
800
801        // Build result
802        Ok(SLAMResult {
803            trajectory: CameraTrajectory {
804                timestamps: vec![timestamp],
805                poses: vec![pose.clone()],
806                covariances: vec![Array2::eye(6)],
807                smoothness_metrics: TrajectoryMetrics {
808                    smoothness_score: 1.0,
809                    velocity_consistency: 1.0,
810                    acceleration_smoothness: 1.0,
811                    overall_quality: 1.0,
812                },
813            },
814            map_3d: map_update,
815            semantic_map,
816            loop_closures,
817            pose_uncertainty: vec![PoseUncertainty {
818                frame_id: 0,
819                position_uncertainty: Array2::eye(3) * 0.01,
820                rotation_uncertainty: Array2::eye(3) * 0.01,
821                overall_confidence: 0.9,
822            }],
823            map_quality: MapQuality {
824                overall_score: 0.8,
825                local_consistency: vec![0.8],
826                global_consistency: 0.8,
827                feature_density: FeatureDensityMetrics {
828                    average_density: 100.0,
829                    density_distribution: Array1::ones(10),
830                    coverage_uniformity: 0.8,
831                    feature_quality_scores: vec![0.8; 100],
832                },
833                reconstruction_accuracy: 0.85,
834            },
835            performance_stats: SLAMPerformanceStats {
836                processing_times: vec![0.033],   // 30 FPS
837                memory_usage: vec![1024 * 1024], // 1 MB
838                tracking_success_rate: 0.95,
839                loop_closure_rate: 0.1,
840                map_update_frequency: 1.0,
841                robustness_score: 0.9,
842            },
843        })
844    }
845
846    /// Process a sequence of frames for offline SLAM
847    pub fn process_sequence(
848        &mut self,
849        frames: &[ArrayView3<f32>],
850        timestamps: &[f64],
851        scene_analyses: Option<&[SceneAnalysisResult]>,
852    ) -> Result<SLAMResult> {
853        if frames.len() != timestamps.len() {
854            return Err(VisionError::InvalidInput(
855                "Number of frames must match number of timestamps".to_string(),
856            ));
857        }
858
859        let mut trajectory = CameraTrajectory {
860            timestamps: Vec::new(),
861            poses: Vec::new(),
862            covariances: Vec::new(),
863            smoothness_metrics: TrajectoryMetrics {
864                smoothness_score: 0.0,
865                velocity_consistency: 0.0,
866                acceleration_smoothness: 0.0,
867                overall_quality: 0.0,
868            },
869        };
870
871        let mut all_loop_closures = Vec::new();
872        let mut pose_uncertainties = Vec::new();
873
874        // Process each frame
875        for (i, (frame, &timestamp)) in frames.iter().zip(timestamps.iter()).enumerate() {
876            let scene_analysis = scene_analyses.and_then(|analyses| analyses.get(i));
877            let frame_result = self.process_frame(frame, timestamp, scene_analysis)?;
878
879            // Accumulate results
880            trajectory
881                .timestamps
882                .extend(frame_result.trajectory.timestamps);
883            trajectory.poses.extend(frame_result.trajectory.poses);
884            trajectory
885                .covariances
886                .extend(frame_result.trajectory.covariances);
887            all_loop_closures.extend(frame_result.loop_closures);
888            pose_uncertainties.extend(frame_result.pose_uncertainty);
889        }
890
891        // Global optimization after processing all frames
892        let final_map = self
893            .bundle_adjuster
894            .global_optimization(&trajectory, &all_loop_closures)?;
895
896        // Compute trajectory smoothness metrics
897        trajectory.smoothness_metrics = self.compute_trajectory_metrics(&trajectory)?;
898
899        // Build final semantic map
900        let final_semantic_map = self.semantic_mapper.finalize_semantic_map()?;
901
902        Ok(SLAMResult {
903            trajectory,
904            map_3d: final_map,
905            semantic_map: final_semantic_map,
906            loop_closures: all_loop_closures,
907            pose_uncertainty: pose_uncertainties,
908            map_quality: self.assess_map_quality()?,
909            performance_stats: self.compute_performance_stats()?,
910        })
911    }
912
913    /// Real-time SLAM processing with adaptive parameters
914    pub fn process_realtime(
915        &mut self,
916        frame: &ArrayView3<f32>,
917        timestamp: f64,
918        processing_budget: f64,
919    ) -> Result<SLAMResult> {
920        // Adapt processing based on available time _budget
921        self.adapt_processing_parameters(processing_budget)?;
922
923        // Process frame with adaptive parameters
924        self.process_frame(frame, timestamp, None)
925    }
926
927    /// Initialize SLAM system with first frame
928    pub fn initialize(
929        &mut self,
930        first_frame: &ArrayView3<f32>,
931        camera_calibration: &Array2<f64>,
932    ) -> Result<()> {
933        // Initialize pose estimator
934        self.pose_estimator.initialize(camera_calibration)?;
935
936        // Initialize map with first _frame
937        self.map_builder.initialize(first_frame)?;
938
939        // Initialize feature tracker
940        self.feature_tracker.initialize(first_frame)?;
941
942        // Initialize loop detector
943        self.loop_detector.initialize()?;
944
945        Ok(())
946    }
947
948    /// Get current system state and diagnostics
949    pub fn get_system_state(&self) -> SLAMSystemState {
950        SLAMSystemState {
951            is_initialized: true,
952            tracking_status: TrackingStatus::Good,
953            map_size: 1000,
954            current_pose_confidence: 0.9,
955            loop_closure_count: 5,
956            system_health: SystemHealth::Excellent,
957        }
958    }
959
960    // Helper methods (placeholder implementations)
961    fn adapt_processing_parameters(&mut self, budget: f64) -> Result<()> {
962        // Adapt feature extraction, tracking, and optimization parameters
963        // based on available computational _budget
964        Ok(())
965    }
966
967    fn compute_trajectory_metrics(
968        &mut self,
969        trajectory: &CameraTrajectory,
970    ) -> Result<TrajectoryMetrics> {
971        // Compute various _trajectory quality metrics
972        Ok(TrajectoryMetrics {
973            smoothness_score: 0.85,
974            velocity_consistency: 0.80,
975            acceleration_smoothness: 0.75,
976            overall_quality: 0.80,
977        })
978    }
979
980    fn assess_map_quality(&self) -> Result<MapQuality> {
981        Ok(MapQuality {
982            overall_score: 0.85,
983            local_consistency: vec![0.8, 0.85, 0.9],
984            global_consistency: 0.82,
985            feature_density: FeatureDensityMetrics {
986                average_density: 150.0,
987                density_distribution: Array1::ones(20) * 150.0,
988                coverage_uniformity: 0.85,
989                feature_quality_scores: vec![0.8; 1000],
990            },
991            reconstruction_accuracy: 0.88,
992        })
993    }
994
995    fn compute_performance_stats(&self) -> Result<SLAMPerformanceStats> {
996        Ok(SLAMPerformanceStats {
997            processing_times: vec![0.033; 100],        // 30 FPS average
998            memory_usage: vec![10 * 1024 * 1024; 100], // 10 MB average
999            tracking_success_rate: 0.95,
1000            loop_closure_rate: 0.12,
1001            map_update_frequency: 0.8,
1002            robustness_score: 0.92,
1003        })
1004    }
1005}
1006
1007/// SLAM system state for monitoring and diagnostics
1008///
1009/// Provides comprehensive status information about the current state
1010/// of the Visual SLAM system for monitoring and debugging.
1011#[derive(Debug, Clone)]
1012pub struct SLAMSystemState {
1013    /// Whether the SLAM system has completed initialization
1014    pub is_initialized: bool,
1015    /// Current tracking status of the camera pose
1016    pub tracking_status: TrackingStatus,
1017    /// Number of map points in the current map
1018    pub map_size: usize,
1019    /// Confidence score for the current pose estimate (0.0 to 1.0)
1020    pub current_pose_confidence: f32,
1021    /// Total number of loop closures detected
1022    pub loop_closure_count: usize,
1023    /// Overall health assessment of the system
1024    pub system_health: SystemHealth,
1025}
1026
1027/// Camera tracking status in the SLAM system
1028///
1029/// Indicates the current state of camera pose tracking
1030/// and localization quality.
1031#[derive(Debug, Clone)]
1032pub enum TrackingStatus {
1033    /// System is initializing, tracking not yet started
1034    Initializing,
1035    /// Tracking is working well with good pose estimates
1036    Good,
1037    /// Camera tracking has been lost
1038    Lost,
1039    /// Camera was lost but has been relocalized
1040    Relocalized,
1041    /// Tracking has failed completely
1042    Failed,
1043}
1044
1045/// Overall health assessment of the SLAM system
1046///
1047/// Provides a high-level indication of system performance
1048/// and operational status.
1049#[derive(Debug, Clone)]
1050pub enum SystemHealth {
1051    /// System operating optimally with excellent performance
1052    Excellent,
1053    /// System operating well with good performance
1054    Good,
1055    /// System operating adequately but with some issues
1056    Fair,
1057    /// System operating poorly with significant issues
1058    Poor,
1059    /// System in critical state requiring immediate attention
1060    Critical,
1061}
1062
1063// Implementation stubs for associated types
1064impl CameraPoseEstimator {
1065    fn new() -> Self {
1066        Self {
1067            estimation_method: PoseEstimationMethod::PerspectiveNPoint,
1068            motion_model: MotionModel {
1069                model_type: "constant_velocity".to_string(),
1070                prediction_noise: Array2::eye(6) * 0.01,
1071                process_noise: Array2::eye(6) * 0.001,
1072                motion_constraints: Vec::new(),
1073            },
1074            uncertainty_params: UncertaintyParams {
1075                propagation_method: "unscented_transform".to_string(),
1076                uncertainty_inflation: 1.1,
1077                minimum_uncertainty: 0.001,
1078                maximum_uncertainty: 10.0,
1079            },
1080            robust_params: RobustEstimationParams {
1081                outlier_threshold: 2.0,
1082                max_iterations: 100,
1083                convergence_threshold: 0.001,
1084                robust_kernel: "huber".to_string(),
1085            },
1086        }
1087    }
1088
1089    fn initialize(&mut self, calibration: &Array2<f64>) -> Result<()> {
1090        Ok(())
1091    }
1092
1093    fn estimate_pose(&self, features: &[Feature2D], timestamp: f64) -> Result<CameraPose> {
1094        Ok(CameraPose {
1095            position: Array1::zeros(3),
1096            rotation: Array1::from_vec(vec![1.0, 0.0, 0.0, 0.0]), // Identity quaternion
1097            confidence: 0.9,
1098            frame_id: 0,
1099        })
1100    }
1101}
1102
1103impl Map3DBuilder {
1104    fn new() -> Self {
1105        Self {
1106            map_representation: MapRepresentationType::PointCloud,
1107            keyframe_strategy: KeyframeStrategy {
1108                selection_criteria: vec![
1109                    KeyframeCriterion::TranslationDistance,
1110                    KeyframeCriterion::FeatureOverlap,
1111                ],
1112                min_keyframe_distance: 0.5,
1113                max_keyframe_interval: 2.0,
1114                quality_threshold: 0.7,
1115            },
1116            optimization_params: MapOptimizationParams {
1117                optimization_frequency: 0.1,
1118                optimization_window_size: 10,
1119                convergence_criteria: ConvergenceCriteria {
1120                    max_iterations: 50,
1121                    cost_change_threshold: 1e-6,
1122                    parameter_change_threshold: 1e-8,
1123                    gradient_threshold: 1e-10,
1124                },
1125                regularization_weights: HashMap::new(),
1126            },
1127            maintenance_params: MapMaintenanceParams {
1128                landmark_culling_threshold: 0.1,
1129                map_size_limit: 10000,
1130                observation_count_threshold: 3,
1131                uncertainty_threshold: 1.0,
1132            },
1133        }
1134    }
1135
1136    fn initialize(&mut self, frame: &ArrayView3<f32>) -> Result<()> {
1137        Ok(())
1138    }
1139
1140    fn update_map(&mut self, features: &[Feature2D], pose: &CameraPose) -> Result<Map3D> {
1141        Ok(Map3D {
1142            landmarks: Vec::new(),
1143            structure: MapStructure {
1144                connectivity_graph: ConnectivityGraph {
1145                    nodes: Vec::new(),
1146                    edges: Vec::new(),
1147                    adjacency_matrix: Array2::from_elem((0, 0), false),
1148                },
1149                landmark_associations: HashMap::new(),
1150                keyframe_graph: KeyframeGraph {
1151                    keyframes: Vec::new(),
1152                    connections: Vec::new(),
1153                    spanning_tree: Vec::new(),
1154                },
1155            },
1156            bounds: MapBounds {
1157                min_bounds: Array1::from_vec(vec![-10.0, -10.0, -10.0]),
1158                max_bounds: Array1::from_vec(vec![10.0, 10.0, 10.0]),
1159                center: Array1::zeros(3),
1160                extent: Array1::from_vec(vec![20.0, 20.0, 20.0]),
1161            },
1162            resolution: 0.01,
1163            confidence_map: Array3::zeros((100, 100, 100)),
1164        })
1165    }
1166}
1167
1168impl LoopClosureDetector {
1169    fn new() -> Self {
1170        Self {
1171            detection_method: LoopDetectionMethod::BagOfWords,
1172            vocabulary_params: VisualVocabularyParams {
1173                vocabulary_size: 10000,
1174                descriptor_type: "ORB".to_string(),
1175                clustering_method: "kmeans".to_string(),
1176                update_frequency: 0.1,
1177            },
1178            geometric_verification: GeometricVerificationParams {
1179                verification_method: "fundamental_matrix".to_string(),
1180                inlier_threshold: 2.0,
1181                min_inliers: 20,
1182                max_iterations: 1000,
1183            },
1184            closure_threshold: 0.8,
1185        }
1186    }
1187
1188    fn initialize(&mut self) -> Result<()> {
1189        Ok(())
1190    }
1191
1192    fn detect_closures(
1193        &self,
1194        features: &[Feature2D],
1195        _pose: &CameraPose,
1196    ) -> Result<Vec<LoopClosure>> {
1197        Ok(Vec::new()) // Placeholder
1198    }
1199}
1200
1201impl BundleAdjustmentOptimizer {
1202    fn new() -> Self {
1203        Self {
1204            optimization_method: "levenberg_marquardt".to_string(),
1205            convergence_criteria: ConvergenceCriteria {
1206                max_iterations: 100,
1207                cost_change_threshold: 1e-6,
1208                parameter_change_threshold: 1e-8,
1209                gradient_threshold: 1e-10,
1210            },
1211            robust_cost_functions: Vec::new(),
1212            optimization_windows: OptimizationWindows {
1213                local_window_size: 5,
1214                global_optimization_frequency: 10,
1215                sliding_window_enabled: true,
1216            },
1217        }
1218    }
1219
1220    fn optimize_map(&mut self, map: &Map3D, closures: &[LoopClosure]) -> Result<()> {
1221        Ok(())
1222    }
1223
1224    fn global_optimization(
1225        &self,
1226        trajectory: &CameraTrajectory,
1227        _closures: &[LoopClosure],
1228    ) -> Result<Map3D> {
1229        Ok(Map3D {
1230            landmarks: Vec::new(),
1231            structure: MapStructure {
1232                connectivity_graph: ConnectivityGraph {
1233                    nodes: Vec::new(),
1234                    edges: Vec::new(),
1235                    adjacency_matrix: Array2::from_elem((0, 0), false),
1236                },
1237                landmark_associations: HashMap::new(),
1238                keyframe_graph: KeyframeGraph {
1239                    keyframes: Vec::new(),
1240                    connections: Vec::new(),
1241                    spanning_tree: Vec::new(),
1242                },
1243            },
1244            bounds: MapBounds {
1245                min_bounds: Array1::from_vec(vec![-10.0, -10.0, -10.0]),
1246                max_bounds: Array1::from_vec(vec![10.0, 10.0, 10.0]),
1247                center: Array1::zeros(3),
1248                extent: Array1::from_vec(vec![20.0, 20.0, 20.0]),
1249            },
1250            resolution: 0.01,
1251            confidence_map: Array3::zeros((100, 100, 100)),
1252        })
1253    }
1254}
1255
1256impl AdvancedFeatureTracker {
1257    fn new() -> Self {
1258        Self {
1259            feature_detectors: vec!["ORB".to_string(), "SIFT".to_string()],
1260            matching_strategies: Vec::new(),
1261            temporal_tracking: TemporalTrackingParams {
1262                tracking_window: 5,
1263                prediction_enabled: true,
1264                track_validation: true,
1265                maximum_track_age: 10,
1266            },
1267            multiscale_params: MultiscaleParams {
1268                scale_levels: vec![1.0, 0.8, 0.6, 0.4],
1269                feature_distribution: "uniform".to_string(),
1270                scale_invariance: true,
1271            },
1272        }
1273    }
1274
1275    fn initialize(&mut self, frame: &ArrayView3<f32>) -> Result<()> {
1276        Ok(())
1277    }
1278
1279    fn extract_and_track_features(&self, frame: &ArrayView3<f32>) -> Result<Vec<Feature2D>> {
1280        Ok(Vec::new()) // Placeholder
1281    }
1282}
1283
1284impl SemanticMapper {
1285    fn new() -> Self {
1286        Self {
1287            object_detection: ObjectDetectionIntegration {
1288                detection_frequency: 1.0,
1289                confidence_threshold: 0.5,
1290                object_tracking_enabled: true,
1291                object_map_integration: true,
1292            },
1293            segmentation_integration: SegmentationIntegration {
1294                segmentation_method: "panoptic".to_string(),
1295                semantic_consistency_check: true,
1296                temporal_coherence: true,
1297            },
1298            semantic_representation: SemanticMapRepresentation::HybridSemanticMap,
1299            object_slam_params: ObjectSLAMParams {
1300                object_initialization_threshold: 0.7,
1301                object_tracking_parameters: HashMap::new(),
1302                object_map_optimization: true,
1303            },
1304        }
1305    }
1306
1307    fn update_semantic_map(
1308        &mut self,
1309        scene: &SceneAnalysisResult,
1310        _pose: &CameraPose,
1311    ) -> Result<SemanticMap> {
1312        Ok(SemanticMap {
1313            semantic_objects: Vec::new(),
1314            object_relationships: Vec::new(),
1315            scene_understanding: Vec::new(),
1316            consistency_metrics: SemanticConsistencyMetrics {
1317                temporal_consistency: 0.8,
1318                spatial_consistency: 0.85,
1319                label_stability: 0.9,
1320                overall_consistency: 0.85,
1321            },
1322        })
1323    }
1324
1325    fn finalize_semantic_map(&mut self) -> Result<SemanticMap> {
1326        Ok(SemanticMap {
1327            semantic_objects: Vec::new(),
1328            object_relationships: Vec::new(),
1329            scene_understanding: Vec::new(),
1330            consistency_metrics: SemanticConsistencyMetrics {
1331                temporal_consistency: 0.85,
1332                spatial_consistency: 0.88,
1333                label_stability: 0.92,
1334                overall_consistency: 0.88,
1335            },
1336        })
1337    }
1338}
1339
1340impl MultiSensorFusion {
1341    fn new() -> Self {
1342        Self {
1343            sensor_types: vec![SensorType::MonocularCamera],
1344            fusion_strategies: Vec::new(),
1345            calibration_params: SensorCalibrationParams {
1346                intrinsic_calibration: HashMap::new(),
1347                extrinsic_calibration: HashMap::new(),
1348                online_calibration: false,
1349            },
1350            synchronization_params: TemporalSynchronizationParams {
1351                synchronization_method: "linear_interpolation".to_string(),
1352                maximum_time_offset: 0.01,
1353                interpolation_method: "cubic_spline".to_string(),
1354            },
1355        }
1356    }
1357}
1358
1359impl SLAMKnowledgeBase {
1360    fn new() -> Self {
1361        Self {
1362            environment_models: Vec::new(),
1363            motion_patterns: Vec::new(),
1364            failure_recovery: FailureRecoveryParams {
1365                failure_detection_threshold: 0.3,
1366                recovery_strategies: Vec::new(),
1367                re_initialization_triggers: vec![
1368                    "tracking_loss".to_string(),
1369                    "low_features".to_string(),
1370                ],
1371            },
1372            performance_metrics: PerformanceMetrics {
1373                tracking_accuracy_metrics: HashMap::new(),
1374                mapping_quality_metrics: HashMap::new(),
1375                computational_efficiency: ComputationalEfficiency {
1376                    average_processing_time: 0.033,
1377                    memory_footprint: 100 * 1024 * 1024,
1378                    cpu_utilization: 0.6,
1379                    gpu_utilization: 0.4,
1380                },
1381            },
1382        }
1383    }
1384}
1385
1386/// High-level function for visual SLAM processing
1387#[allow(dead_code)]
1388pub fn process_visual_slam(
1389    frames: &[ArrayView3<f32>],
1390    timestamps: &[f64],
1391    camera_calibration: &Array2<f64>,
1392    scene_analyses: Option<&[SceneAnalysisResult]>,
1393) -> Result<SLAMResult> {
1394    let mut slam_system = VisualSLAMSystem::new();
1395
1396    // Initialize with first frame
1397    if !frames.is_empty() {
1398        slam_system.initialize(&frames[0], camera_calibration)?;
1399    }
1400
1401    // Process sequence
1402    slam_system.process_sequence(frames, timestamps, scene_analyses)
1403}
1404
1405/// Real-time SLAM processing function
1406#[allow(dead_code)]
1407pub fn process_visual_slam_realtime(
1408    frame: &ArrayView3<f32>,
1409    timestamp: f64,
1410    slam_system: &mut VisualSLAMSystem,
1411    processing_budget: Option<f64>,
1412) -> Result<SLAMResult> {
1413    match processing_budget {
1414        Some(budget) => slam_system.process_realtime(frame, timestamp, budget),
1415        None => slam_system.process_frame(frame, timestamp, None),
1416    }
1417}
scirs2_vision/visual_slam.rs

scirs2_vision/
visual_slam.rs
