BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

TL;DR

BIEVR-LIO introduces a robust LiDAR-Inertial Odometry method using high-resolution voxel maps and informed sampling to enhance accuracy and reliability in challenging environments.

Contribution

It proposes a novel voxel-based map representation and sampling strategy that improve robustness and efficiency of LiDAR-Inertial Odometry in sparse and uninformative environments.

Findings

Achieves state-of-the-art performance in well-constrained scenes.

Shows substantial robustness improvements in challenging scenarios.

Enables downstream tasks like elevation mapping.

Abstract

Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to constrain point cloud registration, resulting in degraded LiDAR-Inertial Odometry (LIO) accuracy or even divergence. To address this, we present BIEVR-LIO, a novel approach designed specifically to exploit subtle variations in the available geometry for improved robustness. We propose a high-resolution map representation that stores surfaces as compact voxel-wise oriented height images. This representation can directly be used for registration without the calculation of intermediate geometric primitives while still supporting efficient updates. Since informative geometry is often sparsely distributed in the environment, we further propose a map-informed point sampling strategy to focus registration on geometrically informative regions, improving robustness in uninformative environments while reducing computational cost compared to global high-resolution sampling. Experiments across multiple sensors, platforms, and environments demonstrates state-of-the-art performance in well-constrained scenes and substantial improvements in challenging scenarios where baseline methods diverge. Additionally, we demonstrate that the fine-grained geometry captured by BIEVR-LIO can be used for downstream tasks such as elevation mapping for robot locomotion.