Title :
A novel fusion method for robot indoor environment mapping
Author :
Huo Guanglei ; Li Ruifeng ; Zhao Lijun ; Wang Ke
Author_Institution :
State Key Lab. of Robot. & Syst., Harbin Inst. of Technol., Harbin, China
Abstract :
In order to solve the stability of mapping of mobile robot, a fusion method based on Kalman Filter is proposed to reduce the accumulative errors during the mobile motion. This fusion method, which can fuse the sequential scan matching results and odometer measures, is suitable for raw points based scan matching method. In this paper, pose estimation results from raw points based scan matching method are viewed as the observation model, and odometer measures are viewed as the status model. Experimental results are shown to validate the effectiveness of the proposed approach.
Keywords :
Kalman filters; SLAM (robots); distance measurement; indoor environment; mobile robots; sensor fusion; Kalman filter; accumulative error reduction; fusion method; mobile motion; mobile robot mapping stability; observation model; odometer measures; pose estimation; raw point based scan matching method; robot indoor environment mapping; sequential scan matching; status model; Estimation; Iterative closest point algorithm; Mobile robots; Robot kinematics; Simultaneous localization and mapping;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on
DOI :
10.1109/ROBIO.2014.7090717
