Register two point clouds using phase correlation
computes the rigid transformation that registers the moving point cloud,
tform = pcregistercorr(
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.
additionally returns the peak correlation value of the phase difference between
the two occupancy grids.
peak] = pcregistercorr(___)
[___] = pcregistercorr(___,
specifies options using one or more name-value arguments in addition to the
input arguments in previous syntaxes. For example,
'Window',false sets the
name-value argument to
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)
gridSize— Size of square occupancy grid
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).
gridStep— Size of each grid cell
Size of each grid cell, specified as a scalar value in world units.
'zlim',[0 3]sets the Z-axis lower limit to
0and the upper limit to
zlim— Z-axis limit
[0 3](default) | vector of form [zmin zmax]
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
Window— Logical to specify whether to use windowing
Logical to specify whether to use windowing to suppress spectral
leakage effects in the frequency domain. When you set
true, the function
uses a Blackman window to increase the stability of registration
results. If the common features that will be aligned in the occupancy
grids are oriented along the edges, then setting
false could provide
superior registration results.
tform— Rigid transformation
Rigid transformation, returned as a
rigid3d object. The rigid transformation registers a moving
point cloud to a fixed point cloud. The
describes the rigid 3-D transform.
rmse— Root mean square error
Root mean square error, returned as the Euclidean distance between the aligned point clouds.
peak— Peak correlation value
Peak correlation value of the phase difference between the two occupancy
grids, returned as a scalar value. A peak value less than
0.03 indicates a poor registration result.
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
If you obtain poor registration results and the
correlation value is less than
0.03, try setting the
Window argument to
 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.