To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy.

No abstract

The field of mobile robotics has seen significant growth regarding the use of indoor laser mapping technology, but most two-dimensional Light Detection and Ranging (2D LiDAR) can only scan a plane of fixed height, and it is difficult to obtain the information of objects below the fixed height, so inaccurate environmental mapping and navigation mis-collision problems easily occur. Although three-dimensional (3D) LiDAR is also gradually applied, it is less used in indoor mapping because it is more expensive and requires a large amount of memory and computation. Therefore, a laser data compensation algorithm based on indoor depth map enhancement is proposed in this paper. Firstly, the depth map acquired by the depth camera is removed and smoothed by bilateral filters to achieve the enhancement of depth map data, and the multi-layer projection transformation is performed to reduce the dimension to compress it into pseudo-laser data. Secondly, the pseudo-laser data are used to remap the laser data according to the positional relationship between the two sensors and the obstacle. Finally, the fused laser data are added to the simultaneous localization and mapping (SLAM) front-end matching to achieve multi-level data fusion. The performance of the multi-sensor fusion before and after is compared with that of the existing fusion scheme via simulation and in kind. The experimental results show that the fusion algorithm can achieve a more comprehensive perception of environmental information and effectively improve the accuracy of map building.

No abstract