Research On Trajectory Restoration Considering Occlusion In Lighthouse Environment

Lighthouse is a high-precision centimeter-level positioning system using infrared lasers.Due to the use of infrared lasers,it is easy to be blocked by obstacles,so that all or part of the infrared sensors cannot capture the scan of the machine frame.When the system cannot obtain more than three sensor read values,it cannot obtain the accurate position according to the positioning algorithm.However,the traditional location algorithm can not deal with the occlusion effectively,which makes the composition of the trajectory prone to errors.This paper briefly introduces the existing indoor positioning methods and algorithms.Most of the above indoor positioning systems do not consider the issue of occlusion.Firstly,in order to solve the problem of track recovery under occlusion condition,based on kalman filtering,this paper studies the trajectory filling problem of kalman filtering in which some or all of the observed values are lost randomly under occlusion condition.Firstly,the kalman filter is improved by local observation loss,and the update of kalman filter is derived by local observation.Through these local observations,the iteration of Kalman filtering algorithm and its error covariance matrix becomes random.To solve the problem of trajectory recovery under occlusion,the experiment extracts data from the moving target under the same occlusion condition,and performs trajectory filling through the traditional kalman filtering algorithm and the improved Kalman filtering algorithm.The experimental results show that the improved kalman filtering algorithm has higher accuracy.Secondly,in order to improve the accuracy of moving target trajectory under occlusion condition,based on the improved Kalman filter trajectory filling algorithm,multi-sensor assisted trajectory filling is integrated,and acceleration sensor assisted trajectory filling under occlusion condition and electronic compass information is used to assist trajectory filling under occlusion condition.In this paper,a Kalman filter is designed to process acceleration information,and a trajectory filling system based on Kalman filter algorithm is realized By analyzing the information of the electronic compass,the trajectory filling system of the kalman filter algorithm which fuses the information of the electronic compass is realized.In view of the above algorithm,the experiment extracts data from the same occlusion condition of moving objects,and fills the trajectories through the above algorithms respectively,and compares with the improved Kalman filter algorithm.The experimental results show that the improved Kalman filter algorithm with multi-sensor fusion has obvious improvement in trajectory accuracy.