Name
Abstract | ||
|---|---|---|
This paper addresses the problem of visual-inertial odometry (VIO) with a downward facing monocular camera when a micro aerial vehicle (MAV) flying at high altitude (over 100 meters). It is important to note that large scene depth causes visual motion constraints significantly less informative than that in near-sighted scenarios as considered in most existing VIO methods. To cope with this challenge, we develop an efficient MSCKF-based VIO algorithm aided by a single 1D laser range finder (LRF), termed LRF-VIO, which runs in real time on an embedded system. The key idea of the proposed LRF-VIO is to fully exploit the limited metric distance information provided by the 1D LRF to disambiguate the scale during visual feature tracking, thus improving the VIO performance at high altitude. Specifically, during the MSCKF visual measurement update, we deliberately constrain the depth of those SLAM features co-planar with the single LRF measuring point. Additionally, delayed initialization of features utilizes the LRF measurements whenever possible, and online extrinsic calibration between the LRF and monocular camera is performed to further improve estimation accuracy and robustness. The proposed LRF-VIO is extensively validated in both indoor and outdoor real-world experiments, outperforming the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICRA46639.2022.9811757 | IEEE International Conference on Robotics and Automation |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaxin Hu | 1 | 0 | 0.34 |
| Jun Hu | 2 | 0 | 0.34 |
| Yunjun Shen | 3 | 0 | 0.34 |
| Xiaoming Lang | 4 | 0 | 0.34 |
| Bo Zang | 5 | 0 | 0.34 |
| Guoquan Huang | 6 | 0 | 1.01 |
| Yinian Mao | 7 | 0 | 1.01 |