Plane Extraction Based Localization
This application:
- Reads in two point clouds, the first of which is a smaller RGB point cloud within a space and the second which is a model point cloud.
- Performs Statistical Outlier Removal on both point clouds
- Uses a voxelgrid to filter each point cloud
- Identify planes from each cloud and then identify clusters on the planar surface that are either extending or regressing into the plane
- Segment the clusters and identify keypoints
- Generate features at those keypoints including using the normals and the average color of the plane the object is on
- Match keypoints between the model and the test point cloud and display their correspondences
Missing features/TODO:
- Integrate into ROS (Currently standalone so that it is faster to test with the same dataset)
- Output the location in the model space as determined by the strongest correspondences between features of keypoints (Very simple to implement, only need to pinpoint the location and then scale the distances to the model space)
- Implement rotating the head and tilting to provide more accuracy with initial localization (HERB's head is not great and will be upgraded soon)
- Update periodically relocalization within the model when no other tasks are running to increase accuracy after the robot or camera has moved.
- Ubuntu 14.04 or newer
- Cmake version 2.8 or higher
- PCL v1.2 or higher
- Kevin Zhang [email protected] [email protected]
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Download PEBL source
git clone https:/firephinx/PEBL.git cd PEBL cmake CMakeLists.txt make