Lidar Scan matching EKF-SLAM using the differential model of vehicle motion

Abstract

Simultaneous localization and mapping is a mobile robot positioning themselves and creating the map of the environment at the same time, which is the core problem of the vehicle achieve the authentic intelligent. EKF-SLAM is a widely used SLAM algorithm based on the extended Kaiman Alter. The EKF-SLAM proposed in this paper based on the differential model of vehicle motion, which consider the vehicle trajectory as many small straight Une segments. The algorithm effectively reduce the positioning error compared with the dead reckoning and has more simplified and generic model compared with the EKF-SLAM algorithm based on vehicle kinematics model. Meanwhile, it has a lower requirements on the hardware acquisition system. The algorithm is more robust than the traditional EKF-SLAM So the algorithm will have a certain reference value on the SLAM research and provide a new way on the SLAM research based on the differential model of vehicle motion.