feat(tier4_perception_launch): downsample perception input pointcloud #6886
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Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com>
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Co-authored-by: Yukihiro Saito <yukky.saito@gmail.com>
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…autowarefoundation#6886) * feat: downsample perception input pointcloud Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com> * fix: add group if to switch downsample node Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com> * fix: add test and exec depend Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com> * Update launch/tier4_perception_launch/launch/perception.launch.xml Co-authored-by: Yukihiro Saito <yukky.saito@gmail.com> * chore: refactor perception.launch.xml Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com> --------- Signed-off-by: yoshiri <yoshiyoshidetteiu@gmail.com> Co-authored-by: Yukihiro Saito <yukky.saito@gmail.com> Signed-off-by: vividf <yihsiang.fang@tier4.jp>
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Description
This PR enables user to use downsampled pointcloud for whole perception process.
Recently, fast downsample filter was implemented by @yukkysaito in #6138.
With improvements in sensor performance, the number of point clouds from LiDAR is expected to continue to increase. Especially in large vehicles equipped with multiple LiDARs, it has been found that there is significant latency in subsequent processing (mainly ground segmentation), which is why there is a desire to limit the number of point clouds as much as possible.
Currently, there are no modules in perception that require dense point clouds, and in centerpoint, having too many input point clouds can lead to decreased recognition performance due to constraints on the number of input point clouds. Therefore, it is proposed to downsample the point clouds used in perception at the earliest stage.
Related links
Tests performed
See perception common scenario results
Tested with xx1-gen2 vehicle in TIER IV.
Notes for reviewers
Interface changes
Effects on system behavior
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