ODTFormer: Efficient Obstacle Detection and Tracking with Stereo Cameras Based on Transformer
This addresses efficient obstacle perception for autonomous robots, offering a significant computational improvement over existing methods.
The paper tackles obstacle detection and tracking for robot navigation by proposing ODTFormer, a Transformer-based model that achieves state-of-the-art performance in detection and comparable tracking accuracy with 10-20 times less computation cost.
Obstacle detection and tracking represent a critical component in robot autonomous navigation. In this paper, we propose ODTFormer, a Transformer-based model to address both obstacle detection and tracking problems. For the detection task, our approach leverages deformable attention to construct a 3D cost volume, which is decoded progressively in the form of voxel occupancy grids. We further track the obstacles by matching the voxels between consecutive frames. The entire model can be optimized in an end-to-end manner. Through extensive experiments on DrivingStereo and KITTI benchmarks, our model achieves state-of-the-art performance in the obstacle detection task. We also report comparable accuracy to state-of-the-art obstacle tracking models while requiring only a fraction of their computation cost, typically ten-fold to twenty-fold less. The code and model weights will be publicly released.