Adaptive Keyframe Generation based LiDAR Inertial Odometry for Complex Underground Environments

Abstract

In this paper, we present a LiDAR Inertial Odometry (LIO) algorithm utilizing adaptive keyframe generation which achieves fast and accurate state estimation for aerial and ground robots. It is known that keyframe generation significantly affects the performance of Simultaneous Localization and Mapping (SLAM) algorithms. Unlike existing SLAM algorithms that generate keyframes based on fixed conditions, we propose to use adaptive keyframe generation conditions considering characteristics of surrounding environment using real-time LiDAR scans. When a keyframe is generated, the keyframe and the corresponding LiDAR measurements are stored in our novel data structure designed for efficient sub- map generation. The scan to sub-map matching module then uses the Generalized Iterative Closest Point (GICP) algorithm to adjust estimated states at a global scale, producing more accurate and globally consistent state estimation results even in large-scale underground environments. Experimental results from diverse types of underground environments show that the proposed method outperforms the existing state-of-the-art LIO algorithms in various metrics such as computational speed, CPU usage, and accuracy.