A multi-sensor fusion positioning approach for indoor mobile robot using factor graph

Abstract

Navigation and positioning based on sensor fusion has received great attention in low computational complexity, high positioning accuracy and good robustness for indoor mobile robots. This paper presents a multi-sensor fusion factor graph (MSF-FG) positioning method applying IMU, Odometer and LiDAR sensors. By taking advantage of the sensor characteristics and using factor graph theory, a MSF-FG positioning model is constructed to improve positioning accuracy and reduce computational complexity. In addition, an adaptive function is designed to improve the robustness of the system by dynamically adjusting the weight of each factor. Meanwhile, the proposed algorithm is derived by Gauss-Newton and Levenberg-Marquardt methods. Simulation and experimental results show that compared with the conventional inertial navigation system (INS) and extended Kalman filter (EKF) algorithms, the proposed MSF-FG positioning method not only reduces the mean location error by about 40%, but also reduces the computational complexity and enhances the stability of the system.