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feat: accelerate voxel filter #10566
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feat: accelerate voxel filter #10566
manato
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autowarefoundation:main
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manato:feat/accelerate_voxel_filter
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Signed-off-by: Manato HIRABAYASHI <[email protected]>
Signed-off-by: Manato HIRABAYASHI <[email protected]>
…ic on pointer to void Signed-off-by: Manato HIRABAYASHI <[email protected]>
Signed-off-by: Manato HIRABAYASHI <[email protected]>
Thank you for contributing to the Autoware project! 🚧 If your pull request is in progress, switch it to draft mode. Please ensure:
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Set output format to `Cloud XYZIRC` according to the [design document](https://autowarefoundation.github.io/autoware-documentation/main/design/autoware-architecture/sensing/data-types/point-cloud/) Signed-off-by: Manato HIRABAYASHI <[email protected]>
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Labels
component:sensing
Data acquisition from sensors, drivers, preprocessing. (auto-assigned)
run:build-and-test-differential
Mark to enable build-and-test-differential workflow. (used-by-ci)
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Description
This PR adds a CUDA-accelerated voxel grid downsample implementation.
Related links
Parent Issue:
How was this PR tested?
After this launch, a node named
/cuda_voxel_grid_downsample_filtrer
subscribes/sensing/lidar/top/pointcloud_raw
as input and publishes/sensing/lidar/top/downsampled
as outputThe following figure shows execution time comparison with

autoware::pointcloud_preprocessor::FasterVoxelGridDownsampleFilter
(denoted asbefore
) and the implementation in this PR (denoted asafter
).Notes for reviewers
I qualitatively confirmed that the output of this PR implementation is almost identical to the ones from
autoware::pointcloud_preprocessor::FasterVoxelGridDownsampleFilter
. However, I also observed that the number of output points is sometimes different (1 point difference), which I concluded comes from numerical computation differenceInterface changes
None.
Effects on system behavior
None.