TY - GEN
T1 - Multi-Sensor Fusion Based Indoor Localization and Navigation System for Dual-Ackerman Steering Mobile Robots
AU - Lu, Yufei
AU - Hao, Qun
AU - Zhang, Shaohui
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Simultaneous localization and mapping (SLAM) has been playing an important role in robotics perception, due to its ability to simultaneously localize the robot's pose and build maps of the surrounding environment. Concurrently, path planning algorithms are widely utilized to achieve autonomous and efficient robot navigation. However, there remains a lack of robust, flexible, and fully integrated systems that tightly combine both technologies for practical deployment in narrow indoor spaces. To address these issues, we develop a multi-sensor fusion-based indoor localization and navigation system, and propose a flexible motion control algorithm specifically tailored for dual-Ackermann steering vehicles. Real-world experimental results demonstrate that our method achieves robust indoor localization and mapping, while enabling highly flexible steering and accurate navigation. The average translational Euclidean error of the system's navigation remains at 6.8 cm.
AB - Simultaneous localization and mapping (SLAM) has been playing an important role in robotics perception, due to its ability to simultaneously localize the robot's pose and build maps of the surrounding environment. Concurrently, path planning algorithms are widely utilized to achieve autonomous and efficient robot navigation. However, there remains a lack of robust, flexible, and fully integrated systems that tightly combine both technologies for practical deployment in narrow indoor spaces. To address these issues, we develop a multi-sensor fusion-based indoor localization and navigation system, and propose a flexible motion control algorithm specifically tailored for dual-Ackermann steering vehicles. Real-world experimental results demonstrate that our method achieves robust indoor localization and mapping, while enabling highly flexible steering and accurate navigation. The average translational Euclidean error of the system's navigation remains at 6.8 cm.
KW - mobile robots
KW - motion control
KW - navigation
KW - simultaneous localization and mapping (SLAM)
UR - https://www.scopus.com/pages/publications/105042906854
U2 - 10.1109/EECR69522.2026.11548898
DO - 10.1109/EECR69522.2026.11548898
M3 - Conference contribution
AN - SCOPUS:105042906854
T3 - 2026 12th International Conference on Electrical Engineering, Control and Robotics, EECR 2026
SP - 273
EP - 279
BT - 2026 12th International Conference on Electrical Engineering, Control and Robotics, EECR 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Electrical Engineering, Control and Robotics, EECR 2026
Y2 - 6 April 2026 through 8 April 2026
ER -