feat(autoware_lidar_bevfusion): implementation of bevusion using tensorrt

[knzo25]

Description

This PR introduces BEVFusion to autoware using TensorRT.
I would like to ask reviewers to let the "integration" into the pipeline/launchers to a posterior PR 🙏

Related links

Parent Issue:

• Link

How was this PR tested?

Notes for reviewers

The onnx files can be found here: TIER IV INTERNAL LINK. The models will be uploaded to a public link as the last part of the review (we are currently facing issues about the best way to distribute them without affecting CI/CD and image sizes...)

Since this package introduces early fusion, it can not directly be integrated into autoware (the lidar-only model can). Such integration should be relegated to the next PR to avoid increasing unnecessarily the number of stakeholders on this PR.

To test the PR, I recommend using the taxi project (will omit the launch command), and launch bevfusion separatedly.

ros2 launch autoware_lidar_bevfusion lidar_bevfusion.launch.xml

For now, the models must be placed in the config folder, and to change the modality (default is camera-lidar), the `yaml' file can be modified. This is the yaml file parameters needed for the lidar-only model.

/**:
  ros__parameters:
    # modality
    sensor_fusion: false
    # non-network params
    max_camera_lidar_delay: 0.12
    # plugins
    plugins_path: $(find-pkg-share autoware_lidar_bevfusion)/plugins/libautoware_tensorrt_plugins.so
    # network
    trt_precision: fp16
    cloud_capacity: 2000000
    onnx_path: "$(var model_path)/bevfusion_lidar_v2.onnx"
    engine_path: "$(var model_path)/bevfusion_lidar_v2.engine"
    # pre-process params
    densification_num_past_frames: 0
    densification_world_frame_id: map
    # post-process params
    circle_nms_dist_threshold: 0.5
    iou_nms_target_class_names: ["CAR"]
    iou_nms_search_distance_2d: 10.0
    iou_nms_threshold: 0.1
    yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0] # refers to the class_names
    score_threshold: 0.1

Interface changes

None.

Effects on system behavior

None.

[github-actions]

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[xmfcx]

• Is this related to feat(autoware_tensorrt_bevdet): add new 3d object detection method #7956 ?

[knzo25]

@xmfcx
Although both use viewtransform, they are different papers and modalities
https://arxiv.org/pdf/2112.11790
https://arxiv.org/pdf/2205.13542

[knzo25]

As TensorRT was upgraded and spconv was added (autowarefoundation/autoware#5794), I will be opening this PR 🎉

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 26.26%. Comparing base (c3134c2) to head (fb1ac42).

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #10024      +/-   ##
==========================================
+ Coverage   26.24%   26.26%   +0.02%     
==========================================
  Files        1378     1378              
  Lines      107445   107468      +23     
  Branches    41428    41433       +5     
==========================================
+ Hits        28194    28222      +28     
+ Misses      76433    76425       -8     
- Partials     2818     2821       +3     

Flag	Coverage Δ		*Carryforward flag
differential	3.32% <ø> (?)
differential-cuda	2.23% <ø> (?)
total	26.26% <ø> (+0.02%)	⬆️	Carriedforward from e00b2af

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[knzo25]

@amadeuszsz
As discussed internally, I pasted the links and how to execute the model. Please let me know if you need anything 🙏