  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.
