Register two point clouds using phase correlation
computes the rigid transformation that registers the moving point cloud,
tform
= pcregistercorr(moving
,fixed
,gridSize
,gridStep
)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.
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.
[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.
pcdenoise
| pcdownsample
| pcfitplane
| pcmerge
| pcregistercpd
| pcregisterndt
| pcshow
| pcshowpair
| pctransform