Detecting Objects in Point Clouds Using ROS 2 and TAO-PointPillars

Presenting a ROS 2 node for 3D object detection in point clouds using a pretrained model from NVIDIA TAO Toolkit based on PointPillars.

Accurate, fast object detection is a key task for safe robotic navigation and this node takes advantage of performing detection on lidar input over image-based systems (lidar is not sensitive to changing lighting conditions, unlike cameras). The node takes point clouds as input from real or simulated lidars, performs TensorRT-optimized inference to detect objects and outputs 3D bounding boxes as a Detection3DArray message for each point cloud.

The PointPillar model we used detects objects of three classes: Vehicle, Pedestrian, and Cyclist. You can train your own detection model following the TAO toolkit steps and use it with this node!

For more details, check out the technical blog and NVIDIA-AI-IOT/ros2_tao_pointpillars on GitHub.


Great work. Does it have ROS-1 version?

No, we have only tested it for ROS 2.

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