Tightly-Coupled Perception and Navigation of Heterogeneous Land-Air Robots in Complex Scenarios
Yufeng Yue, Mingxing Wen, Yosmar Putra, Meiling Wang, Danwei Wang
Abstract
In unstructured and unknown environments, heterogeneous robots must be able to perceive the environment, coordinate with each other and complete tasks collaboratively with onboard sensors. In this paper, a tightly-coupled perception and navigation framework is proposed for heterogeneous land-air robots, which forms a closed loop of perception-navigation for heterogeneous robots. The key novelty of this work is the proposing of a unified framework to formulate the cooperative mapping and navigation problem, as well as the derivation of high-level coordination strategy and low-level goal-oriented navigation within a fully integrated approach. To provide a comprehensive understanding of the environment, a flexible probabilistic map fusion algorithm is applied to merge local maps generated by hybrid robots. The proposed UAV-UGV hybrid system is validated in challenging experiments, proving its robustness and effectiveness in practical tasks.
BibTeX
@inproceedings{icra2021_tightlycoupledpe,
title = {Tightly-Coupled Perception and Navigation of Heterogeneous Land-Air Robots in Complex Scenarios},
author = {Yufeng Yue and Mingxing Wen and Yosmar Putra and Meiling Wang and Danwei Wang},
booktitle = {ICRA 2021},
year = {2021}
}