Abstract
In this article we present a new method for multi-robot cooperative target tracking based on dynamic baseline stereo vision. The core novelty of our approach includes a computationally light-weight scheme to compute the 3D stereo measurements that exactly satisfy the epipolar constraints and a covariance intersection (CI)-based method to fuse the 3D measurements obtained by each individual robot. Using CI we are able to systematically integrate the robot localization uncertainties as well as the uncertainties in the measurements generated by the monocular camera images from each individual robot into the resulting stereo measurements. Through an extensive set of simulation and real robot results we show the robustness and accuracy of our approach with respect to ground truth. The source code related to this article is publicly accessible on our website and the datasets are available on request.
| Original language | English |
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| Title of host publication | FUSION 2016 - 19th International Conference on Information Fusion, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1728-1734 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780996452748 |
| Publication status | Published - 2016 Aug 1 |
| Externally published | Yes |
| Event | 19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany Duration: 2016 Jul 5 → 2016 Jul 8 |
Other
| Other | 19th International Conference on Information Fusion, FUSION 2016 |
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| Country/Territory | Germany |
| City | Heidelberg |
| Period | 16/7/5 → 16/7/8 |
Keywords
- cooperative target tracking
- cooperative visual perception
- dynamic baseline stereo vision
- multi robot systems
- multiple flying robots
ASJC Scopus subject areas
- Statistics, Probability and Uncertainty
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Signal Processing