Work

ODTFormer: Efficient Obstacle Detection and Tracking with Stereo Cameras Based on Transformer

Year: 2024

Type: conference-paper

Abstract: Obstacle detection and tracking represent a critical component in robot autonomous navigation. In this paper, we propose ODTFormer, a Transformer-based model that addresses 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. Our code is available on https://github.com/neu-vi/ODTFormer.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros58592.2024.10802476

Authors: Tianye Ding, Hongyu Li, Huaizu Jiang

Institutions: Northeastern University, Brown University

Language: English

Topic: Advanced Vision and Imaging

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, U.S. National Library of Medicine

Awards: A New Informatics Approach for Detection of Cerebrovascular Abnormalities, RI: Small: Toward Efficient and Robust Dynamic Scene Understanding Based on Visual Correspondences