| Autor: |
Chi Zhang, Zhong Yang, Luwei Liao, Yulong You, Yaoyu Sui, Tang Zhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Machines, Vol 10, Iss 3, p 181 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2075-1702 |
| DOI: |
10.3390/machines10030181 |
| Popis: |
Pose estimation and environmental perception are the fundamental capabilities of autonomous robots. In this paper, a novel real-time pose estimation and object detection (RPEOD) strategy for aerial robot target tracking is presented. The aerial robot is equipped with a binocular fisheye camera for pose estimation and a depth camera to capture the spatial position of the tracked target. The RPEOD system uses a sparse optical flow algorithm to track image corner features, and the local bundle adjustment is restricted in a sliding window. Ulteriorly, we proposed YZNet, a lightweight neural inference structure, and took it as the backbone in YOLOV5 (the state-of-the-art real-time object detector). The RPEOD system can dramatically reduce the computational complexity in reprojection error minimization and the neural network inference process; Thus, it can calculate real-time on the onboard computer carried by the aerial robot. The RPEOD system is evaluated using both simulated and real-world experiments, demonstrating clear advantages over state-of-the-art approaches, and is significantly more fast. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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