The Method Of Autonomous Navigation For MAV Based On LIDAR/Optical Flow/IMU

At present,MAV relies heavily on GPS.Once the GPS signal is disturbed or lost,the reliable and accurate navigation information can be no longer provided by the navigation system relying on GPS.In this case,the aircraft will often be out of control and accidents will be caused.In the GPS-denied environment,other sensors are usually needed by the autonomous flight of the aircraft to detect changes in the surroundings to provide relative positioning information.The navigation system using optical flow,which was inspired by insect's visual positioning,has been gradually matured in recent years as a mainstream solution for indoor positioning of aircraft.However,under the special circumstances such as light changes and high-speed carrier movements,there will still be positioning failures.LIDAR has been widely used in SLAM because of its high accuracy of distance measurement and LIDAR SLAM can provide accurate navigation and positioning information for aircraft.This paper focuses on LIDAR/optical flow/IMU integrated navigation technology in GPS-denied environment for MAV.Firstly,the principle of optical flow algorithm,LIDAR SLAM algorithm and their estimation errors under different conditions is analyzed in this paper.For the optical flow algorithm,the positioning error under the change of lighting condition and the ‘large velocity' of MAV is analyzed.For the LIDAR SLAM algorithm,the reason of its positioning error under the featureless environment and the ‘large velocity' of MAV is analyzed.In order to improve the anti-jamming performance and positioning accuracy of the optical flow algorithm under different lighting conditions and "large velocity" of the aircraft,the optical flow algorithm under GPS-denied environment is improved in the paper.On the basis of ORB LK pyramid optical flow algorithm,the influence of implementing image segmentation,filtering preprocessing and template matching on anti-jamming performance of the optical flow algorithm,is studied.The static and dynamic experiments proves that the proposed mothod performed significantly better than the original optical flow algorithm,in terms of positioning accuracy and anti-jamming performance.To improve the accuracy and reliability of autonomous navigation algorithms,the research on LIDAR/optical flow/IMU integrated navigation technology is carried out in this paper.Firstly,the LIDAR/IMU integrated filter algorithm based on Kalman filter is studied,during which the loosely integrated method is proved to be effective by semi-physical experiments.Then the optimization iteration principle of LIDAR SLAM algorithm is analyzed,and a tightly integrated filtering method of LIDAR/IMU based on initial pose predictive compensation is proposed.The inertial sensor is used to assist the process of pose calculation in LIDAR SLAM.The results of the semi-physical experiments demonstrates that the tightly integrated method can enhance the effectiveness and reliability of the integrated navigation system.Considering the fault-tolerant performance of the whole autonomous navigation system,the integrated method of LIDAR/optical flow/IMU based on resettable federal filter is studied.Finally,an experimental verification platform of the integrated navigation algorithm of LIDAR/optical flow/IMU for MAV is built in this paper.In order to deal with the collected data off-line,the software development platform based on ROS robot operating system is used.The experiment results show that the method proposed in this paper can be used as a guide for the MAV in the GPS-denied environment,and the reliability of the integrated navigation method of LIDAR/optical flow/IMU is verified by the actual flight experiment.Comparing with the current optical flow and LIDAR SLAM poisoning method,the error properties of the navigating system positioning method is analized and improved in this paper.And different from the two sensors work on the vehicle independently,the two navigating systems are combined with the fault-tolerant performance and applied to MAV in this paper.The robustness of the whole navigating system is improved.