The ICP filter uses the PCL’s Iterative Closest Point (ICP) algorithm to calculate a rigid (rotation and translation) transformation that best aligns two datasets. The first input to the ICP filter is considered the “fixed” points, and all subsequent points are “moving” points. The output from the change filter are the “moving” points after the calculated transformation has been applied, one point view per input. The transformation matrix is inserted into the stage’s metadata.


ICP requires that the initial pose of the two point sets to be adequately close, which are not always available, especially when transformation is non-rigid. ICP can handle limited nonrigid transformations but be aware ICP may be unable to escape a local minimum. Consider using CPD instead.

From [Xuechen2019]:

ICP starts with an initial guess of the transformation between the two point sets and then iterates between finding the correspondence under the current transformation and updating the transformation with the newly found correspondence. ICP is widely used because it is rather straightforward and easy to implement in practice; however, its biggest problem is that it does not guarantee finding the globally optimal transformation. In fact, ICP converges within a very small basin in the parameter space, and it easily becomes trapped in local minima. Therefore, the results of ICP are very sensitive to the initialization, especially when high levels of noise and large proportions of outliers exist.

Dynamic Plugin

This stage requires a dynamic plugin to operate


        "type": "filters.icp"

To get the transform matrix, you’ll need to use the --metadata option from the pipeline command:

$ pdal pipeline icp-pipeline.json --metadata icp-metadata.json

The metadata output might start something like:

            "converged": true,
            "fitness": 0.01953125097,
            "transform": "           1  2.60209e-18 -1.97906e-09       -0.375  8.9407e-08            1  5.58794e-09      -0.5625 6.98492e -10 -5.58794e-09            1   0.00411987           0            0            0            1"

See also

filters.transformation to apply a transform to other points. filters.cpd for the use of a probabilistic assignment of correspondences between pointsets.