TY - GEN
T1 - Aerial-Ground Robots Collaborative 3D Mapping in GNSS-Denied Environments
AU - Yue, Yufeng
AU - Zhao, Chunyang
AU - Wang, Yuanzhe
AU - Yang, Yi
AU - Wang, Danwei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Collaborative heterogeneous robots are expected to perform comprehensive perception, mapping and coordination in search and rescue scenarios. The challenge of collaboration between heterogeneous robots lies in their huge differences in perception, mobility and processing capabilities. In this paper, a novel collaborative UAV-UGV mapping framework is proposed in GNSS-denied and unknown environments. The key novelty of this work is the proposing of a unified framework to formulate the UAV-UGV collaborative mapping problem with a continuous-discrete model, as well as its realization in real robotic systems. In order to project continuous space into discrete space, a novel information gain trigger scheme is pro-posed. The continuous space allows each robot to perform high frequency local map estimation, while discrete space describes the problem of multi-resolution hybrid map fusion. Considering the nature of data heterogeneity, a flexible probabilistic fusion algorithm is proposed that addresses the multi-resolution hybrid map fusion problem, where the local maps generated by UAV and UGV are fused based on Bayesian rule. The proposed UAV-UGV hybrid system is validated in various challenging scenarios, demonstrating its accuracy and utility in practical tasks.
AB - Collaborative heterogeneous robots are expected to perform comprehensive perception, mapping and coordination in search and rescue scenarios. The challenge of collaboration between heterogeneous robots lies in their huge differences in perception, mobility and processing capabilities. In this paper, a novel collaborative UAV-UGV mapping framework is proposed in GNSS-denied and unknown environments. The key novelty of this work is the proposing of a unified framework to formulate the UAV-UGV collaborative mapping problem with a continuous-discrete model, as well as its realization in real robotic systems. In order to project continuous space into discrete space, a novel information gain trigger scheme is pro-posed. The continuous space allows each robot to perform high frequency local map estimation, while discrete space describes the problem of multi-resolution hybrid map fusion. Considering the nature of data heterogeneity, a flexible probabilistic fusion algorithm is proposed that addresses the multi-resolution hybrid map fusion problem, where the local maps generated by UAV and UGV are fused based on Bayesian rule. The proposed UAV-UGV hybrid system is validated in various challenging scenarios, demonstrating its accuracy and utility in practical tasks.
UR - http://www.scopus.com/inward/record.url?scp=85136326852&partnerID=8YFLogxK
U2 - 10.1109/ICRA46639.2022.9812319
DO - 10.1109/ICRA46639.2022.9812319
M3 - Conference contribution
AN - SCOPUS:85136326852
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 10041
EP - 10047
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -