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Localization map based on normal distributions transform (NDT)


    The pcmapndt object creates a normal distributions transform (NDT) map from a prebuilt point cloud map of the environment. The NDT map is a compressed, memory-efficient representation suitable for localization. The object converts the point cloud map into a set of voxels (3-D boxes), each represented by a 3-D normal distribution. Use the selectSubmap object function to select a submap within the map from a coarse position estimate. Use the findPose object function to localize the pose of the sensor based on the assembled map.




    ndtMap = pcmapndt(ptCloudMap,voxelSize) returns an NDT map from a point cloud map, ptCloudMap.


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    This property is read-only.

    Currently selected submap, specified as a 6-element vector of the form [xmin,xmax ymin ymax zmin zmax] that describes the range of the submap along each axis. The elements of the vector describe the region of interest represented by the submap.

    This property is read-only.

    Size of the voxels, specified as a scalar value in world units.

    This property is read-only.

    Range of the map along the x-axis, specified as a 2-element vector of the form [xmin xmax] .

    This property is read-only.

    Range of the map along the Y-axis, specified as a 2-element vector of the form [ymin ymax] .

    This property is read-only.

    Range of the map along the z-axis, specified as a 2-element vector of the form [zmin zmax] .

    This property is read-only.

    Mean value of each voxel, specified as an M-by-3 matrix. Each row of the matrix contains the [x y z] values for a voxel. M is the number of voxels.

    This property is read-only.

    Covariance of each voxel, specified as a 3-by-3-by-M array for M voxels.

    This property is read-only.

    Number of points in each voxel, specified as an M-by-1 vector for M voxels.

    Object Functions

    selectSubmapSelect submap within map
    isInsideSubmapCheck if query position is inside selected submap
    findPoseLocalize a point cloud within a map using the normal distributions transform (NDT) algorithm
    showVisualize normal distributions transform (NDT) map


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    Load a normal distributions transform (NDT) map from a MAT file.

    data = load('ndtMapParkingLot.mat');
    ndtMap = data.ndtMapParkingLot;

    Load point cloud scans and pose estimates from a second MAT file.

    data = load('parkingLotData.mat');
    ptCloudScans = data.parkingLotData.ptCloudScans;
    initPoseEsts = data.parkingLotData.initPoseEsts;

    Display the NDT map.


    Change the viewing angle to top-view.


    Select the submap centered around the first estimate.

    center = initPoseEsts(1).Translation;
    sz = [70 50 20];
    ndtMap = selectSubmap(ndtMap,center,sz);

    Set the radius for visualization of the current location and the distance threshold to update the submap.

    radius = 0.5;
    distThresh = 15;

    Loop over the point clouds, localize them in the map, and update the selected submap as needed.

    numScans   = numel(ptCloudScans);
    for n = 1:numScans
        ptCloud = ptCloudScans(n);
        initPose = initPoseEsts(n);
        poseTranslation = initPose.Translation;
        [isInside,distToEdge] = isInsideSubmap(ndtMap,poseTranslation);
        submapNeedsUpdate = ~isInside ...       % Current pose is outside submap
            || any(distToEdge(1:2) < distThresh);   % Current pose is close to submap edge
    if submapNeedsUpdate
        ndtMap = selectSubmap(ndtMap,poseTranslation,sz);
    % Localize the point cloud scan in the map.
    currPose = findPose(ndtMap,ptCloud,initPose);
    % Display the position of the estimate as a circle.
    pos = [currPose.Translation(1:2) radius]; 
    % Pause to view the change.

    Figure contains an axes. The axes contains an object of type scatter.


    Biber, P., and W. Strasser. “The Normal Distributions Transform: A New Approach to Laser Scan Matching.” In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) Vol. 3, 2743–48. Las Vegas, Nevada, USA: IEEE, 2003.

    [1] Magnusson, Martin. "The Three-Dimensional Normal-Distributions Transform: An Efficient Representation forRegistration, Surface Analysis, and Loop Detection." PhD thesis, Örebro universitet, 2009. urn:nbn:se:oru:diva-8458.

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