Name
Abstract | ||
|---|---|---|
Traditional simultaneous localization and mapping (SLAM) approaches that utilize visible cameras or light detection and rangings (LiDARs) frequently fail in dusty, low-textured, or completely dark environments. To address this problem, this study proposes a novel approach by tightly coupling perception data from a thermal infrared camera and a LiDAR based on the advantages of the former. However, applying a thermal infrared camera directly to existing SLAM frameworks is difficult because of the sensor differences. Thus, a new infrared-visual odometry method is developed by utilizing edge points as features to ensure the robustness of the state estimation. Furthermore, an edge-based infrared-LiDAR SLAM framework is developed to generate a dense depth map for recovering visual scale and to provide real-time pose estimation at the same time throughout the day. An infrared-visual and LiDAR-integrated place recognition method is also introduced to achieve robust loop closure. Finally, several experiments are performed to illustrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/rob.22040 | JOURNAL OF FIELD ROBOTICS |
Keywords | DocType | Volume |
3D robotic mapping, GPS denied, SLAM | Journal | 39 |
Issue | ISSN | Citations |
2 | 1556-4959 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqiang Chen | 1 | 0 | 0.34 |
| Yu Wang | 2 | 0 | 0.34 |
| Haoyao Chen | 3 | 0 | 0.34 |
| Liu YH | 4 | 1540 | 185.05 |