1.Guilin University of Technology;2.Guangxi Natural Resources Information Center;3.Institute of Artificial Intelligence and Big Data Application,Guangxi Industry Research Institute
LiDAR has the advantages of strong anti-interference capability and high speed, angle and distance resolution in indoor positioning, but its accuracy is easily affected by the interference of environmental factors during the positioning process. In this paper, we propose an indoor positioning method for LiDAR and PDR fusion, based on the Extended Kalman Filter (EKF), by solving the displacement increments and angular observations of LiDAR and the positional information of PDR, so that the two can be complementarily fused. The problems of non-line-of-sight influence and error accumulation are effectively suppressed, and the positioning accuracy of single-class combination algorithm and fusion-class combination algorithm are compared and analyzed. The experimental results show that the LiDAR and PDR fusion localization algorithm is effective in improving the accuracy and stability compared with the single localization method when the indoor personnel are walking. The localization error is 0.98m for PDR and 0.6m for LiDAR, and the localization error can be reduced to 0.32m after EKF filtering and fusion.
李景文,韦晶闪,陆妍玲,等. 一种LiDAR和PDR融合的室内目标定位方法[J]. 科学技术与工程, , ():复制