Adaptive Denoising-Enhanced LiDAR Odometry for Degeneration Resilience in Diverse Terrains

TL;DR

This paper introduces an adaptive LiDAR odometry method that dynamically assesses environmental degeneration to improve robustness and accuracy in diverse, high-noise terrains by selectively filtering point cloud data.

Contribution

It proposes a novel adaptive clustering and point filtering approach that adjusts to environmental degeneration levels, enhancing LiDAR odometry resilience.

Findings

Demonstrates higher accuracy on KITTI benchmark

Shows improved robustness in real environments

Effectively filters unstable features in high-noise settings

Abstract

The flexibility of Simultaneous Localization and Mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effective module of SLAM frameworks. However, reducing the amount of point cloud data can lead to potential loss of information and possible degeneration. As a result, this research proposes a LiDAR odometry that can dynamically assess the point cloud's reliability. The algorithm aims to improve adaptability in diverse settings by selecting important feature points with sensitivity to the level of environmental degeneration. Firstly, a fast adaptive Euclidean clustering algorithm based on range image is proposed, which, combined with depth clustering, extracts the primary structural points of the environment defined as ambient skeleton points. Then, the environmental degeneration level is computed through the dense normal features of the skeleton points, and the point cloud cleaning is dynamically adjusted accordingly. The algorithm is validated on the KITTI benchmark and real environments, demonstrating higher accuracy and robustness in different environments.