TY - GEN
T1 - A Multi-Precision Indoor Localization Strategy Based on Hybrid Vive and Adaptive Monte Carlo Method
AU - Hsiao, Tesheng
AU - Sheu, Shih Jie
AU - He, Rusheng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The virtual reality (VR) motion tracking devices, such as the HTC Vive system, offer a low-cost, high-precision indoor positioning solution. However, Vive is applicable in a limited space, which does not fit the requirements of mobile robots. On the other hand, the simultaneous localization and mapping (SLAM) algorithms give a low-precision positioning in a wider area. In this paper, we propose a hybrid localization strategy that combines the advantages of Vive and SLAM such that a mobile robot can carry out delicate tasks around a workstation that requires high-precision positioning, and moves among workstations in a wide space with low-precision. To guarantee smooth and reliable transition from the low-precision to the high-precision area, we extend the workspace of Vive with additional lighthouses, and use the result of visual odometry to improve the robustness of Vive system after the tracker reconnection. Then we do experiments to verify the precision of the Vive system and the proposed hybrid localization strategy.
AB - The virtual reality (VR) motion tracking devices, such as the HTC Vive system, offer a low-cost, high-precision indoor positioning solution. However, Vive is applicable in a limited space, which does not fit the requirements of mobile robots. On the other hand, the simultaneous localization and mapping (SLAM) algorithms give a low-precision positioning in a wider area. In this paper, we propose a hybrid localization strategy that combines the advantages of Vive and SLAM such that a mobile robot can carry out delicate tasks around a workstation that requires high-precision positioning, and moves among workstations in a wide space with low-precision. To guarantee smooth and reliable transition from the low-precision to the high-precision area, we extend the workspace of Vive with additional lighthouses, and use the result of visual odometry to improve the robustness of Vive system after the tracker reconnection. Then we do experiments to verify the precision of the Vive system and the proposed hybrid localization strategy.
KW - HTC Vive
KW - adaptive Monte Carlo localization (AMCL)
KW - gmapping
KW - indoor position system (IPS)
KW - mobile robots
UR - http://www.scopus.com/inward/record.url?scp=85144633928&partnerID=8YFLogxK
U2 - 10.1109/CACS55319.2022.9969805
DO - 10.1109/CACS55319.2022.9969805
M3 - Conference contribution
AN - SCOPUS:85144633928
T3 - 2022 International Automatic Control Conference, CACS 2022
BT - 2022 International Automatic Control Conference, CACS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Automatic Control Conference, CACS 2022
Y2 - 3 November 2022 through 6 November 2022
ER -