Search and rescue under the forest canopy using multiple UAVs

作     者：Tian, Yulun Liu, Katherine Ok, Kyel Tran, Loc Allen, Danette Roy, Nicholas How, Jonathan P. 

作者机构：MIT 70 Vassar St Cambridge MA 02139 USA NASA Langley Res Ctr Hampton VA 23665 USA 

出 版 物：《INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH》 (国际机器人研究杂志)

年 卷 期：2020年第39卷第10-11期

页      面：1201-1221页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：NASA Convergent Aeronautics Solutions project Design Environment for Novel Vertical Lift Vehicles (DELIVER) ONR (BRC award) [N000141712072] ARL DCIST [W911NF-17-2-0181] U.S. Department of Defense (DOD) [N000141712072] Funding Source: U.S. Department of Defense (DOD) 

主　　题：Multi-robot systems field robotics SLAM 

摘      要：We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.