An autonomous UAV system based on adaptive LiDAR Inertial Odometry for
practical exploration in complex environments
Abstract
Unmanned aerial vehicles (UAVs) offer many advantages over
ground vehicles, including quadruped robots, based on high
maneuverability when performing exploration in complex and unknown
environments. However, due to their limited computational capability,
UAVs require light-weight but accurate state estimation algorithms for
reliable exploration. In this paper, we propose an segmented map based
exploration system based on LiDAR-based state estimation for UAVs. The
proposed system includes capabilities such as exploration, obstacle
avoidance, and object detection with localization using 3D dense maps
generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed
system is a hybrid system that can switch between guided and exploration
modes, making it practical for search and rescue missions in disaster
scenarios. The proposed LIO algorithm adapts to its surroundings,
allowing for fast and accurate state estimation in complex environments.
The proposed exploration algorithm is designed to cover specific regions
in the 3D dense map generated by proposed LIO, with the UAV determining
if map points are included within the coverage area. We tested the
proposed system in both simulation and real-world environments and
validated that proposed system outperforms state-of-the-art algorithms
in various aspects such as localization accuracy and exploration
efficiency in complex environments.
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