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tensorrt_bevdet

Purpose

tensorrt_bevdet is a dynamic 3D bev object detection package based on 6 surround view cameras.

Inner-workings / Algorithms

BEVDet is a BEV perception algorithm based on panoramic cameras. It unifies multi-view images into the perspective of BEV for 3D object detection task. In this implementation, BEVDet network to inference with TensorRT.

Inputs / Outputs

Inputs

Name Type Description
~/input/topic_img_fl sensor_msgs::msg::Image input front_left camera image
~/input/topic_img_f sensor_msgs::msg::Image input front camera image
~/input/topic_img_fr sensor_msgs::msg::Image input front_right camera image
~/input/topic_img_bl sensor_msgs::msg::Image input back_left camera image
~/input/topic_img_b sensor_msgs::msg::Image input back camera image
~/input/topic_img_br sensor_msgs::msg::Image input back_right camera image
~/input/topic_img_fl/camera_info sensor_msgs::msg::CameraInfo input front_left camera parameters
~/input/topic_img_f/camera_info sensor_msgs::msg::CameraInfo input front camera parameters
~/input/topic_img_fr/camera_info sensor_msgs::msg::CameraInfo input front_right camera parameters
~/input/topic_img_bl/camera_info sensor_msgs::msg::CameraInfo input back_left camera parameters
~/input/topic_img_b/camera_info sensor_msgs::msg::CameraInfo input back camera parameters
~/input/topic_img_br/camera_info sensor_msgs::msg::CameraInfo input back_right camera parameters

Outputs

Name Type Description
~/output/boxes autoware_perception_msgs::msg::DetectedObjects detected objects

How to Use Tensorrt BEVDet Node

  1. launch tensorrt_bevdet_node

    ros2 launch autoware_tensorrt_bevdet tensorrt_bevdet_node.launch.xml

  2. play ros2 bag of nuScenes data

    Please refer to open source repository ros2_dataset_bridge to publish the ROS 2 topics.

Limitation

The model is trained on open-source dataset NuScenes and has poor generalization on its own dataset, If you want to use this model to infer your data, you need to retrain it.

Trained Models

You can download the onnx format of trained models by clicking on the links below.

The model was trained in NuScenes database for 20 epochs.

If you want to train model using the TIER IV's internal database(~2600 key frames), please refer to the following repositories:BEVDet adapted to TIER IV dataset

References/External links

[1] https://github.com/HuangJunJie2017/BEVDet/tree/dev2.1

[2] https://github.com/LCH1238/BEVDet/tree/export

[3] https://github.com/LCH1238/bevdet-tensorrt-cpp/tree/one