pcregistericp

Register two point clouds using ICP algorithm

Syntax

tform = pcregistericp(moving,fixed)

[tform,movingReg] = pcregistericp(moving,fixed)

[___,rmse] = pcregistericp(moving,fixed)

[___] = pcregistericp(moving,fixed,Name,Value)

Description

tform = pcregistericp(moving,fixed) returns a rigid transformation that registers a moving point cloud to a fixed point cloud.

The registration algorithm is based on the "iterative closest point" (ICP) algorithm. Best performance of this iterative process requires adjusting properties for your data. Consider downsampling point clouds using pcdownsample before using pcregistericp to improve accuracy and efficiency of registration.

Point cloud normals are required by the registration algorithm when you select the 'pointToPlane' metric. Therefore, if the input point cloud’s Normal property is empty, the function fills it. When the function fills the Normal property, it uses 6 points to fit the local plane. Six points may not work under all circumstances. If registration with the 'pointToPlane' metric fails, consider calling the pcnormals function which allows you to select the number of points to use.

[tform,movingReg] = pcregistericp(moving,fixed) additionally returns the transformed point cloud that aligns with the fixed point cloud.

[___,rmse] = pcregistericp(moving,fixed) additionally returns the root mean square error of the Euclidean distance between the inlier aligned points, using any of the preceding syntaxes.

[___] = pcregistericp(moving,fixed,Name,Value) uses additional options specified by one or more Name,Value pair arguments.

Examples

Align Two Point Clouds Using ICP Algorithm

Load point cloud data.

ptCloud = pcread('teapot.ply');

pcshow(ptCloud); 
title('Teapot');

Create a transform object with 30 degree rotation along z -axis and translation [5,5,10].

A = [cos(pi/6) sin(pi/6) 0 0; ...
    -sin(pi/6) cos(pi/6) 0 0; ...
            0         0  1 0; ...
            5         5 10 1];
tform1 = affine3d(A);

Transform the point cloud.

ptCloudTformed = pctransform(ptCloud,tform1);

pcshow(ptCloudTformed);
title('Transformed Teapot');

Apply the rigid registration.

tform = pcregistericp(ptCloudTformed,ptCloud,'Extrapolate',true);

Compare the result with the true transformation.

disp(tform1.T);

    0.8660    0.5000         0         0
   -0.5000    0.8660         0         0
         0         0    1.0000         0
    5.0000    5.0000   10.0000    1.0000

tform2 = invert(tform);
disp(tform2.T);

    0.8660    0.5000    0.0000         0
   -0.5000    0.8660   -0.0000         0
   -0.0000    0.0000    1.0000         0
    5.0000    5.0000   10.0000    1.0000

Input Arguments

moving — Moving point cloud

pointCloud object

Moving point cloud, specified as a pointCloud object.

fixed — Fixed point cloud

pointCloud object

Fixed point cloud, specified as a pointCloud object.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'Metric','pointToPoint' sets the metric for the ICP algorithm to the 'pointToPoint' character vector.

Metric — Minimization metric

'pointToPoint' (default) | 'pointToPlane'

Minimization metric, specified as the comma-separated pair consisting of Metric and the 'pointToPoint' or 'pointToPlane' character vector. The rigid transformation between the moving and fixed point clouds is estimated by the iterative closest point (ICP) algorithm. The ICP algorithm minimizes the distance between the two point clouds according to the given metric.

Setting Metric to 'pointToPlane' can reduce the number of iterations to process. However, this metric requires extra algorithmic steps within each iteration. The 'pointToPlane' metric improves the registration of planar surfaces.

Downsample Method Selection:
Downsample the point clouds using the pcdownsample function. Point cloud normals are required by the registration algorithm when you select the 'pointToPlane'. The 'nonuniformGridSample' algorithm computes the normals on the original data prior to downsampling, resulting in more accurate normals. Therefore, using the 'nonuniformGridSample' downsampling method can result in a more accurate 'pointToPlane' registration. Use the 'random', 'gridAverage', or the 'nonuniformGridSample' input for the pcdownsample function according to the Metric table below.

Metric	Moving PointCloud Downsample Method	Fixed Point Cloud Downsample Method
'pointToPoint'	'random'	'random'
	'gridAverage'	'gridAverage'
'pointToPlane'	'gridAverage'	'gridAverage'
	'random'	'nonuniformGridSample'

Extrapolate — Extrapolation

false (default) | true

Extrapolation, specified as the comma-separated pair consisting of 'Extrapolate' and the boolean true or false. When you set this property to true, the function adds an extrapolation step that traces out a path in the registration state space, that is described in [2]. Setting this property to true can reduce the number of iterations to converge.

InlierRatio — Percentage of inliers

1 (default) | scalar

Percentage of inliers, specified as the comma-separated pair consisting of 'InlierRatio' and a scalar value. Use this value to set a percentage of matched pairs as inliers. A pair of matched points is considered an inlier if its Euclidean distance falls within the percentage set of matching distances. By default, all matching pairs are used.

MaxIterations — Maximum number of iterations

20 (default) | positive integer

Maximum number of iterations, specified as the comma-separated pair consisting of 'MaxIterations' and a positive integer. This value specifies the maximum number of iterations before ICP stops.

Tolerance — Tolerance between consecutive ICP iterations

[0.01, 0.05] (default) | 2-element vector

Tolerance between consecutive ICP iterations, specified as the comma-separated pair consisting of 'Tolerance' and a 2-element vector. The 2-element vector, [Tdiff, Rdiff], represents the tolerance of absolute difference in translation and rotation estimated in consecutive ICP iterations. Tdiff measures the Euclidean distance between two translation vectors. Rdiff measures the angular difference in degrees. The algorithm stops when the average difference between estimated rigid transformations in the three most recent consecutive iterations falls below the specified tolerance value.

InitialTransform — Initial rigid transformation

rigid3d object

Initial rigid transformation, specified as the comma-separated pair consisting of 'InitialTransform' and a rigid3d object. The initial rigid transformation is useful when you provide an external coarse estimation.

The rigid3d object contains a translation that moves the center of the moving point cloud to the center of the fixed point cloud.

Verbose — Display progress information

false (default) | true

Display progress information, specified as the comma-separated pair consisting of 'Verbose' and a logical scalar. Set Verbose to true to display progress information.

Output Arguments

tform — Rigid transformation

rigid3d object

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. The iterative closest point (ICP) algorithm estimates the rigid transformation between the moving and fixed point clouds.

movingReg — Transformed point cloud

pointCloud object

Transformed point cloud, returned as a pointCloud object. The transformed point cloud is aligned with the fixed point cloud.

rmse — Root mean square error

positive numeric

Root mean square error, returned as a positive numeric value that represents the Euclidean distance between the inlier aligned points.

Algorithms

Tips

• For ground vehicle point clouds, you can improve performance and accuracy by removing the ground using pcfitplane or segmentGroundFromLidarData before registration. For details on how to do this, see the helper function, helperProcessPointCloud in the Build a Map from Lidar Data (Automated Driving Toolbox) example.

• To merge more than two point clouds, you can use pccat function instead of the pcmerge function.

References

[1] Chen, Y. and G. Medioni. “Object Modelling by Registration of Multiple Range Images.” Image Vision Computing. Butterworth-Heinemann . Vol. 10, Issue 3, April 1992, pp. 145-155.

[2] Besl, Paul J., N. D. McKay. “A Method for Registration of 3-D Shapes.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Los Alamitos, CA: IEEE Computer Society. Vol. 14, Issue 2, 1992, pp. 239-256.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2018a

R2020a: The pcregistericp function returns a rigid3d object

Behavior changed in R2020a

Starting in R2020a, the pcregistericp function returns a rigid3d object. Prior to R2020a, the function returned an affine3d object.