feat(autoware_lidar_centerpoint): added the cuda_blackboard to centerpoint

[knzo25]

Description

This PR is part of a series of PRs that aim to accelerate the Sensing/Perception pipeline through an appropriate use of CUDA.

List of PRs:

• feat(autoware_cuda_pointcloud_preprocessor): a cuda-accelerated pointcloud preprocessor #9454 (pointcloud preprocessing)
• feat(autoware_pointcloud_preprocessor): cuda-accelerated pointcloud concatenation #9455 (concatenation)
• feat(autoware_lidar_centerpoint): added the cuda_blackboard to centerpoint #9453 (centerpoint)
• transfusion (TODO)
• feat(autoware_probabilistic_occupancy_grid_map): cuda accelerated implementation #9542 (OGM - the first implementation will be independent of the blackboard to ease the transition)
• feat: acceleration and transport layer tier4/aip_launcher#348 (aip_launcher)
• feat: acceleration and transport layer sample_sensor_kit_launch#111 (sample_sensor_kit_launch)

To use these branches, the following additions to the autoware.repos are necessary:

  vendor/cuda_blackboard:
    type: git
    url: git@github.com:knzo25/cuda_blackboard.git
    version: main
  vendor/negotiated:
    type: git
    url: https://github.com/osrf/negotiated.git
    version: master

Depending on your machine and how many nodes are in a container, the following branch may also be required:
https://github.com/knzo25/launch_ros/tree/fix/load_composable_node
There seems to be a but in ROS where if you send too many services at once some will be lost and ros_launch can not handle that.

Related links

Parent Issue:

• Link

How was this PR tested?

The sensing/perception pipeline was tested until centerpoint for TIER IV's taxi using the logging simulator.
The following tests were executed in a laptop equipped with a RTX 4060 (laptop) GPU and a Intel(R) Core(TM) Ultra 7 165H (22 cores)

Node / processing time [ms]	Current	PR
/sensing/lidar/top/crop_box_filter_self/debug/processing_time_ms	5.81	N/A
/sensing/lidar/top/crop_box_filter_mirror/debug/processing_time_ms	4.59	N/A
/sensing/lidar/top/distortion_corrector/debug/processing_time_ms	10.96	N/A
/sensing/lidar/top/ring_outlier_filter/debug/processing_time_ms	10.69	N/A
/sensing/lidar/top/cuda_organized_pointcloud_adapter/debug/processing_time_ms	N/A	3.75
/sensing/lidar/top/cuda_pointcloud_preprocessor/debug/processing_time_ms	N/A	1.00
/sensing/lidar/concatenate_data_synchronizer/debug/processing_time_ms	7.83	0.70
Total	38.8	5.45

Notes for reviewers

The main branch that I used for development is feat/cuda_acceleration_and_transport_layer.
However, the changes were too big so I split the PRs. That being said, development, if any will still be on that branch (and then cherrypicked to the respective PRs), and the review changes will be cherrypicked into the development branch.

Interface changes

An additional topic is added to perform type negotiation:
Example: input/pointcloud -> input/pointcloud and input/pointcloud/cuda

Effects on system behavior

Enabling this preprocessing in the launchers should provide a much reduced latency and cpu usage (at the cost of a higher GPU usage)

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[kminoda]

@knzo25 Thank you for your great work 🎉

Do you have any documentation for the cuda_blackboard package? Just a simple API references and overall design in a readme would be helpful to review the PRs.

[knzo25]

@kminoda
Yes, sorry. I am adding some results (processing time, memory consumption, cpu use), documentation, etc as of now, but wanted to open the PRs before to the the ball moving

[knzo25]

@kminoda
Added the cuda_blackboard documentation in https://github.com/knzo25/cuda_blackboard.
As discussed internally, I will create an issue to manage and discuss about this series of PRs