Improved Lidar Odometer Based on Motion Prediction
To address the lidar odometer output trajectory drift problem for a wide range of outdoor building map scenes,a continuous motion prediction algorithm based on the normal distribution transformation is proposed to improve the estimation accuracy of the initial value of point cloud matching under the condition that only lidar is used to construct the odometer.Frame and local map matching is used instead of inter-frame matching.The drift of the motion trajectory is then effectively suppressed.The simulation results are verified by two different scenarios of the Kitti dataset.The improved lidar odometer algorithm reduces the global average errors of two trajectories by 27.93％and 36.66％,while the maximum Z-axis deviation of the two trajectories is reduced by 70.29％and 82.52％.The improved lidar odometer can stably and effectively suppress the motion trajectory drift.
lidar odometernormal distribution transformpoint cloud matchingmotion prediction
NETL
NSTL
万方数据
