Research On LiDAR/IMU Integrated Navigation And Positioning Algorithm

With the continuous innovation and development of science and technology,indoor mobile robots have made great achievements,and its development and application also have broad prospects.When a mobile robot is placed in an unfamiliar environment,its simultaneous localization and map(SLAM)construction technology is a very important research content and direction.In real life,the limitations of a single navigation technology can no longer meet the precise navigation needs in various complex environments.With this demand,more and more scholars at home and abroad are committed to multi-sensor information fusion technology to solve the problem of navigation and positioning.Combined with the research status of LiDAR SLAM and the development trend of multi-sensor fusion,aims to realize the intelligence of mobile robot and improve the positioning accuracy of mobile robot in complex environment,this paper studies the navigation and positioning algorithm of multi-sensor information fusion based on LiDAR and inertial measurement unit(IMU).The main research work is summarized as follows:1)Common coordinate systems in navigation and positioning systems,related working principles of LiDAR and IMU,the coordinate transformation of LiDAR,point cloud matching and basic strapdown inertial algorithms are introduced.The mathematical model of SLAM is described,and introduces the movement model and observation model of mobile robots.2)The LiDAR SLAM algorithm based on Extended Kalman Filter(EKF)is studied.Aiming at the problems of susceptibility to environmental interference and low positioning accuracy when using a single LiDAR sensor,a LiDAR/IMU navigation based on EKF is proposed.The positioning algorithm uses the posture information calculated by the IMU as the initial value of the LiDAR scan matching,and then the posture information of the IMU and LiDAR is fused through the EKF algorithm to obtain the new posture information and fed back to the IMU,and the IMU returns the posture according to the feedback information is compensated for errors,finally,a more accurate positioning algorithm is obtained.By building a simulation verification platform,the feasibility of the proposed algorithm is verified.3)Research on LiDAR/IMU navigation and positioning algorithm based on Rank Kalman Filtering(RKF).EKF algorithm can only linearize the nonlinear system when dealing with the problem of nonlinear system,the defect of this method is that it cannot meet the positioning requirements of indoor mobile robot in various complex environments,a LiDAR/IMU navigation and positioning algorithm based on RKF is proposed.The effectiveness of the proposed algorithm is verified by setting up an experimental scene in real environment and designing two different walking trajectories.The experimental results show that the RKF-based LiDAR/IMU navigation and positioning algorithm has higher positioning accuracy than the EKF-based LiDAR/IMU navigation.