SLAM Algorithm Based on Monocular/IMU/Odometer Fusion

It is common for navigation and positioning accuracy to be reduced when the monocular vision-inertial SLAM algorithm is applied to planar wheeled robots due to additional unobservability. To solve this problem, a tightly-coupled Visual / IMU / Odometer SLAM algorithm is proposed to improve localization accuracy. First, in the visual front-end part, the original image pyramid LK optical flow method is improved, and the rotation information of the gyroscope and the translation information from the odometer are used as priors to optimize the initial optical flow calculation process, thus reducing the calculation amount. Second, IMU / Odometer pre-integral is derived by introducing the wheel odometer information. Finally, odometer constraints are added into the initialization process and back-end nonlinear optimization to realize that vision, IMU, and odometer information are fully integrated. The results of the open-source data set test and car experiment show that the optical flow iteration time of the new algorithm is reduced by about 32郾 5%, and the average positioning error reduced by about 40% compared with that of VINS-Mono. © 2022 China Ordnance Society. All rights reserved.