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The paper introduces BIEVR-LIO, a LiDAR-inertial odometry method that uses a novel high-resolution voxel map representation storing surfaces as oriented height images for direct registration. A map-informed point sampling strategy focuses registration on geometrically informative regions, improving robustness in feature-poor environments. Experiments show state-of-the-art performance and substantial improvements in challenging scenarios where baselines diverge, and demonstrate the utility of the fine-grained geometry for downstream tasks.
LiDAR odometry can be made significantly more robust in challenging, feature-poor environments by representing surfaces as compact voxel-wise oriented height images and focusing registration on geometrically informative regions.
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.
