The pointcloud preprocessing implemented in autoware_pointcloud_preprocessor has been thoroughly tested in autoware. However, the latency it introduces does not scale well with modern LiDAR devices due to the high number of points they introduce.
To alleviate this issue, this package reimplements most of the pipeline presented in autoware_pointcloud_preprocessor leveraging the use of GPGPUs. In particular, this package makes use of CUDA to provide accelerated versions of the already established implementations, while also maintaining compatibility with normal ROS nodes/topics. <!-- cSpell: ignore GPGPUs >
A detailed description of each filter's algorithm is available in the following links.
| Filter Name | Description | Detail |
|---|---|---|
| cuda_pointcloud_preprocessor | Implements the cropping, distortion correction, and outlier filtering (ring-based) of the autoware_pointcloud_preprocessor's CPU versions. |
link |
| cuda_concatenate_and_time_sync_node | Implements pointcloud concatenation an synchronization following autoware_pointcloud_preprocessor's CPU implementation. |
link |
The subsample filters implemented in autoware_pointcloud_preprocessor will have similar counterparts in this package.