TY - GEN
T1 - A Biologically-Inspired Global Localization System for Mobile Robots Using LiDAR Sensor
AU - Zhuang, Genghang
AU - Cagnetta, Carlo
AU - Bing, Zhenshan
AU - Cao, Hu
AU - Li, Xinyi
AU - Huang, Kai
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Localization in the environment is an essential navigational capability for animals and indoor robotic vehicles. In indoor environments, it is still challenging to perfectly solve the global localization problem using probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, an intriguing and promising approach is to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based relocalization approach. The experiment results show that the proposed method is competitive with Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in various localization scenarios.
AB - Localization in the environment is an essential navigational capability for animals and indoor robotic vehicles. In indoor environments, it is still challenging to perfectly solve the global localization problem using probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, an intriguing and promising approach is to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based relocalization approach. The experiment results show that the proposed method is competitive with Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in various localization scenarios.
UR - http://www.scopus.com/inward/record.url?scp=85135372313&partnerID=8YFLogxK
U2 - 10.1109/IV51971.2022.9827283
DO - 10.1109/IV51971.2022.9827283
M3 - Conference contribution
AN - SCOPUS:85135372313
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 984
EP - 990
BT - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
Y2 - 5 June 2022 through 9 June 2022
ER -