Name
Abstract | ||
|---|---|---|
This paper presents a perception-aware path planning framework for unmanned aerial vehicles (UAVs) that explicitly considers perception quality of a light detection and ranging (LiDAR) sensor. The perception quality is quantified based on how scattered feature points are in LiDAR-based simultaneous localization and mapping, which can improve the accuracy of pose estimation of UAVs. In the planning step of a UAV, the proposed framework selects the best path based on the perception quality from a library of candidate paths generated by the rapidly-exploring random trees algorithm. Consequently, the UAV can autonomously fly to a destination in a receding horizon manner. Several simulation trials of the photorealistic environments confirm that our proposed path planner reduces pose estimation error by approximately 85 % on average as compared with a purely-reactive path planner. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MFI55806.2022.9913848 | 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) |
Keywords | DocType | ISBN |
candidate paths,UAV,receding horizon manner,purely-reactive path planner,perception-aware receding horizon path planning,LiDAR-based,perception-aware path planning framework,unmanned aerial vehicles,perception quality,light detection,scattered feature points,planning step | Conference | 978-1-6654-6027-9 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reiya Takemura | 1 | 0 | 0.34 |
| Genya Ishigami | 2 | 0 | 0.34 |