A New Indoor Unmanned Vehicle Navigation System Based on The Combination of Lidar and IMU

Under the condition of indoor denial, the indoor navigation of unmanned ground vehicles is facing great challenges. In order to solve this problem, we introduce a loose integrated navigation system based on lidar and inertial measurement unit (IMU), and propose a new hybrid scanning matching algorithm based on lidar to estimate the relative position and direction change of vehicles. The algorithm combines traversal search and mathematical optimization algorithm. Firstly, the fitness function is introduced into the traversal search algorithm to find the optimal estimated value of vehicle attitude, and then the estimated value is used as the initial value of the numerical optimization algorithm for further optimization. Finally, the lidar estimated attitude is loosely combined with the results of DRS prediction through unscented Kalman filter (UKF), so as to reduce the cumulative error of inertial sensor. The actual experiment is carried out on the self-developed unmanned ground vehicle platform. The results show that the indoor unmanned vehicle navigation system proposed in this paper can effectively reduce the error accumulation of DRS, and the positioning error is reduced by 92.4% compared with hectorslam method.