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pcregistercorr

Register two point clouds using phase correlation

Description

example

tform = pcregistercorr(moving,fixed,gridSize,gridStep) computes the rigid transformation that registers the moving point cloud, moving, to the fixed point cloud, fixed using a phase correlation algorithm.

The function performs registration by first converting both point clouds to a 2-D occupancy grid in the X-Y plane with center at the origin (0,0,0). The occupancy of each grid cell is determined using the Z-coordinate values of points within the grid.

[tform] = pcregistercorr(___,'ZLimit',zlim) sets the Z-axis limits to calculate the occupancy of a grid cell in addition to the input arguments from the previous syntax.

[tform,rmse] = pcregistercorr(___) additionally returns the root mean square error of the Euclidean distance between the registered point clouds.

Examples

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Read data from a Velodyne packet capture (PCAP) file into the workspace.

veloReader = velodyneFileReader('lidarData_ConstructionRoad.pcap','HDL32E');

Read a fixed and a moving point cloud from frames of the lidar data.

frameNumber = 1;
skipFrame   = 5;
fixed  = readFrame(veloReader,frameNumber);
moving = readFrame(veloReader,frameNumber + skipFrame);
 

Find the ground planes for both the moving and the fixed point clouds. Set the maximum distance in meters.

maxDistance = 0.4;
referenceVector = [0 0 1];

groundMoving = pcfitplane(moving,maxDistance,referenceVector);
groundFixed = pcfitplane(fixed,maxDistance,referenceVector);
 

Transform the point clouds so that their ground planes are parallel to the X-Y plane.

tformMoving = normalRotation(groundMoving,referenceVector);
tformFixed = normalRotation(groundFixed,referenceVector);

movingCorrected = pctransform(moving,tformMoving);
fixedCorrected = pctransform(fixed,tformFixed);
 

Register the moving point cloud against the fixed point cloud. Set the occupancy grid size to 100-by-100 meters and the size of each grid cell to 0.5-by-0.5 meters.

gridSize = 100;
gridStep = 0.5;

tform = pcregistercorr(movingCorrected,fixedCorrected,gridSize,gridStep);
 

Transform the moving point cloud using an estimated rigid transformation.

combinedTform = rigid3d(tform.T * tformMoving.T * tformFixed.T);
movingReg = pctransform(moving,combinedTform);
 

Visualize the registration.

figure
subplot(121)
pcshowpair(moving,fixed)
title('Before Registration')
view(2)

subplot(122)
pcshowpair(movingReg,fixed)
title('After Registration')
view(2)

Input Arguments

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Moving point cloud, specified as a pointCloud object.

Fixed point cloud, specified as a pointCloud object.

Size of square occupancy grid, specified as a scalar value in world units. The occupancy grid has both width and height equal to this value. The center is at the origin (0, 0, 0).

Size of each grid cell, specified as a scalar value in world units.

Name-Value Pair Arguments

Example: 'zlim',[0 3] sets the Z-axis lower limit to 0 and the upper limit to 3.

Z-axis limit to compute the occupancy of a grid cell, specified as a vector of the form [zmin zmax], where zmin and zmax are numeric scalars. The function scales points with a Z-axis value from zmin to zmax to probabilities in the range [0, 1]. Values less than zmin are assigned an occupancy value of 0. Values greater than zmax are assigned an occupancy value of 1.

Output Arguments

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Rigid transformation, returned as a rigid3d object. The rigid transformation registers a moving point cloud to a fixed point cloud. The rigid3d object describes the rigid 3-D transform.

Root mean square error, returned as the Euclidean distance between the aligned point clouds.

Tips

  • The phase correlation method is best used to register point clouds when the transformation can be described by a translation in the X-Y plane and a rotation around the Z-axis. For example, a ground vehicle with a horizontally mounted lidar moving on a flat surface.

  • The phase correlation algorithm expects motion to be exclusively along the X-Y plane, as with the ground plane. If motion is not exactly in the X-Y plane, you can use the normalRotation function to transform the point clouds. For example, in vehicular motion, you can reduce the effects of vehicle suspension or surface features such as potholes and speed bumps by using the normalRotation function.

  • Increasing the size of the occupancy grid increases the computational demands of this function. You can control the size of the occupancy grid by modifying the gridSize and gridStep arguments.

References

[1] Dimitrievski, Martin, David Van Hamme, Peter Veelaert, and Wilfried Philips. “Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles.” In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 626–33. Rome, Italy: SCITEPRESS - Science and Technology Publications, 2016.

Introduced in R2020b