A LiDAR Inertial Odometry Based on Adaptive Voxel Grid Filter for Indoor and Outdoor Mixed Environments

Simultaneous localization and mapping (SLAM) is developing rapidly and has received increasing attention for its performance in challenging environments. However, the challenging indoor and outdoor mixed environments are unexpectedly ignored, which are the main application scenarios for robots and low speed vehicles. In this paper, we present a LiDAR inertial odometry based on an adaptive voxel grid filter to cope with point density variations in indoor and outdoor mixed environments. Benefiting from the adaptive voxel grid filter we proposed, our system can adjust the voxel resolution automatically according to indoors or outdoors, improving the system performance in mixed environments. Moreover, we design a dynamic initialization for the adaptive voxel grid filter. The initial voxel size is set from coarse to fine with computational efficiency as feedback. Extensive experiments were performed on the latest public dataset and in the real world, the results demonstrate that our method is effective, achieving higher efficiency of the system while ensuring the same level of accuracy.