Struct fast_slam::FastSLAM[][src]

pub struct FastSLAM<N: RealField, DL: Dim, DF: Dim> where
    DefaultAllocator: Allocator<N, DL, DL> + Allocator<N, DL> + Allocator<N, DF, DF> + Allocator<N, DF>, 
{ pub loc: SampleState<N, DL>, pub map: HashMap<u32, FeatureCondMap<N, DF>>, }

Fast SLAM state.

A sample state of the location with augmented Kalman states of map features.

Fields

loc: SampleState<N, DL>

Location as a sample state.

map: HashMap<u32, FeatureCondMap<N, DF>>

Hashmap of augmented map feature states.

Implementations

impl<N: RealField + ToPrimitive, DL: Dim, DF: DimName> FastSLAM<N, DL, DF> where
    DefaultAllocator: Allocator<N, DL, DL> + Allocator<N, DL> + Allocator<N, DF, DF> + Allocator<N, U1, DF> + Allocator<N, DF>, 
[src]

pub fn new_equal_likelihood(loc: SampleState<N, DL>) -> Self[src]

Creates a FastSLAM with empty feature map.

pub fn observe_new(
    &mut self,
    feature: u32,
    obs_model: impl Fn(&VectorN<N, DL>) -> Feature<N, DF>
)
[src]

SLAM new feature observation (overwrite).

Assumes there is no prior information about the feature (strictly a uniform un-informative prior) This implies: a) There has no information about location so no resampling is requires b) Feature state estimates comes directly from the location and observation

pub fn observe<DZ: Dim>(
    &mut self,
    feature: u32,
    innovation_model: impl Fn(&VectorN<N, DL>, &VectorN<N, DF>) -> (VectorN<N, DZ>, CorrelatedNoise<N, DZ>),
    hx: &MatrixMN<N, DZ, DF>
) where
    DefaultAllocator: Allocator<N, U1, DF> + Allocator<N, DZ, DZ> + Allocator<N, DZ, DF> + Allocator<N, DF, DZ> + Allocator<N, DZ>, 
[src]

SLAM Feature observation. Uses Extended Fast_SLAM observation equations Note: Mathematically only weight ratios are important. Numerically however the range should be restricted. The weights are computed here using the simplest form with common factor Ht removed.

pub fn forget(&mut self, feature: u32) -> Option<FeatureCondMap<N, DF>>[src]

Forget all feature information, feature no can be reused for a new feature

pub fn update_resample(
    &mut self,
    resampler: &mut Resampler,
    roughener: &mut Roughener<N, DL>
) -> Result<(u32, f32), &'static str>
[src]

Resampling Update.

Resample particles using weights. Propagate resampling to All features. Only resamples if weights have been updated.

pub fn statistics_compressed(&self, kstat: &mut KalmanState<N, Dynamic>) where
    DefaultAllocator: Allocator<N, U1, DL>, 
[src]

Compute sample mean and covariance statistics of filter.

kstat elements are filled first with Location statistics and then the Map feature statistics. Feature statistics are are computed in feature number order and only for those for which there is space in kstat.

Note: Covariance values are indeterminate for nparticles ==1 Precond: kstat must have space for Location statistics

pub fn statistics_sparse(&self, kstat: &mut KalmanState<N, Dynamic>) where
    DefaultAllocator: Allocator<N, U1, DL>, 
[src]

Compute sample mean and covariance statistics of filter.

kstat elements are filled first with Location statistics and then the Map feature statistics. Feature statistics are are computed in feature number as index (after location) and only for those for which there is space in kstat.

Note: Covariance values are indeterminate for nparticles ==1 Precond: kstat must have space for Location statistics

Auto Trait Implementations

impl<N, DL, DF> !RefUnwindSafe for FastSLAM<N, DL, DF>

impl<N, DL, DF> !Send for FastSLAM<N, DL, DF>

impl<N, DL, DF> !Sync for FastSLAM<N, DL, DF>

impl<N, DL, DF> !Unpin for FastSLAM<N, DL, DF>

impl<N, DL, DF> !UnwindSafe for FastSLAM<N, DL, DF>

Blanket Implementations

impl<T> Any for T where
    T: 'static + ?Sized
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impl<T> Borrow<T> for T where
    T: ?Sized
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impl<T> BorrowMut<T> for T where
    T: ?Sized
[src]

impl<T> From<T> for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> Same<T> for T

type Output = T

Should always be Self

impl<SS, SP> SupersetOf<SS> for SP where
    SS: SubsetOf<SP>, 

impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
[src]

type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, 
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.

impl<V, T> VZip<V> for T where
    V: MultiLane<T>,