GenZ-ICP: Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting

TL;DR

This paper introduces GenZ-ICP, an adaptive LiDAR odometry method that combines point-to-plane and point-to-point metrics with an adaptive weighting scheme, improving robustness and accuracy across diverse and degenerative environments.

Contribution

The study presents a novel ICP approach that adaptively balances error metrics based on environment geometry, enhancing robustness in degenerative scenarios.

Findings

High adaptability to various environments

Resilience to corridor-like degenerative scenarios

Prevents ill-posed optimization problems

Abstract

Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative environments such as long corridors. This issue stems from the dependence on a single error metric, which has different strengths and weaknesses depending on the geometrical characteristics of the surroundings. To address these problems, this study proposes a novel iterative closest point (ICP) method called GenZ-ICP. We revisited both point-to-plane and point-to-point error metrics and propose a method that leverages their strengths in a complementary manner. Moreover, adaptability to diverse environments was enhanced by utilizing an adaptive weight that is adjusted based on the geometrical characteristics of the surroundings. As demonstrated in our experimental evaluation, the proposed GenZ-ICP exhibits high adaptability to various environments and resilience to optimization degradation in corridor-like degenerative scenarios by preventing ill-posed problems during the optimization process.