Struct opencv::surface_matching::ICP

pub struct ICP { /* fields omitted */ }

This class implements a very efficient and robust variant of the iterative closest point (ICP) algorithm. The task is to register a 3D model (or point cloud) against a set of noisy target data. The variants are put together by myself after certain tests. The task is to be able to match partial, noisy point clouds in cluttered scenes, quickly. You will find that my emphasis is on the performance, while retaining the accuracy. This implementation is based on Tolga Birdal's MATLAB implementation in here: http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme The main contributions come from:

1. Picky ICP: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2003/Zinsser03-ARI.pdf
2. Efficient variants of the ICP Algorithm: http://docs.happycoders.org/orgadoc/graphics/imaging/fasticp_paper.pdf
3. Geometrically Stable Sampling for the ICP Algorithm: https://graphics.stanford.edu/papers/stabicp/stabicp.pdf
4. Multi-resolution registration: http://www.cvl.iis.u-tokyo.ac.jp/~oishi/Papers/Alignment/Jost_MultiResolutionICP_3DIM03.pdf
5. Linearization of Point-to-Plane metric by Kok Lim Low: https://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf

Implementations

impl ICP

pub fn as_raw_ICP(&self) -> *const c_void

pub fn as_raw_mut_ICP(&mut self) -> *mut c_void

impl ICP

pub fn default() -> Result<ICP>

pub fn new(
    iterations: i32,
    tolerence: f32,
    rejection_scale: f32,
    num_levels: i32,
    sample_type: i32,
    num_max_corr: i32
) -> Result<ICP>

\brief ICP constructor with default arguments.

Parameters

• iterations:
• tolerence: Controls the accuracy of registration at each iteration of ICP.
• rejectionScale: Robust outlier rejection is applied for robustness. This value actually corresponds to the standard deviation coefficient. Points with rejectionScale * &sigma are ignored during registration.
• numLevels: Number of pyramid levels to proceed. Deep pyramids increase speed but decrease accuracy. Too coarse pyramids might have computational overhead on top of the inaccurate registrtaion. This parameter should be chosen to optimize a balance. Typical values range from 4 to 10.
• sampleType: Currently this parameter is ignored and only uniform sampling is applied. Leave it as 0.
• numMaxCorr: Currently this parameter is ignored and only PickyICP is applied. Leave it as 1.

C++ default parameters

• tolerence: 0.05f
• rejection_scale: 2.5f
• num_levels: 6
• sample_type: ICP::ICP_SAMPLING_TYPE_UNIFORM
• num_max_corr: 1

Trait Implementations

impl Boxed for ICP

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for ICP

fn drop(&mut self)

Executes the destructor for this type.

impl ICPTrait for ICP

fn as_raw_ICP(&self) -> *const c_void

fn as_raw_mut_ICP(&mut self) -> *mut c_void

fn register_model_to_scene(
    &mut self,
    src_pc: &Mat,
    dst_pc: &Mat,
    residual: &mut f64,
    pose: &mut Matx44d
) -> Result<i32>

\brief Perform registration

fn register_model_to_scene_vec(
    &mut self,
    src_pc: &Mat,
    dst_pc: &Mat,
    poses: &mut Vector<Pose3DPtr>
) -> Result<i32>

\brief Perform registration with multiple initial poses

impl Send for ICP