A ROS package for feature extraction through PCL. The features include tall, cylindrical objects such as light posts or trees.
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- pcl
Feature extraction is performed as follows:
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The point cloud is rotated into a local-level frame based on the roll/pitch of the LiDAR.
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The point cloud is filtered based on user defined cartesian thresholds (min/max xyz).
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Features (aka keypoints) are detected from the filtered point cloud (as desribed in the following section).
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A descriptor is formed for each keypoint based on the neighbors (points within a radius). The neighboring points are gathered from the full, unfiltered point cloud.
a) For each of the 16 channels, segmentation is performed conditioned on
- min/max number of points
- cluster tolerance (min distance from one cluster to the next)
- cluster radius threshold (max size of a cluster)
b) From the resulting clusters, segmentation is performed once again to group clusters belonging to the same object. This secondary segmentation is conditioned on
- min number of points (num_detection_channels) to reject non-cylindrical objects
- cluster tolerance (same as before)
c) Features are projected to a 2D space (with z=0)