BIEVR-LIO：通过凸起图像增强体素地图实现鲁棒的激光雷达惯性里程计
BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps
AI总结：
本文提出BIEVR-LIO方法，通过高分辨率体素地图表示和信息导向点采样策略，提升在信息稀疏环境下的激光雷达惯性里程计鲁棒性，并在多种场景中实现优异性能。
摘要：
可靠的里程计对于移动机器人至关重要，因为它们越来越多地进入更具挑战性的环境，这些环境通常包含很少的信息来约束点云配准，导致激光雷达惯性里程计（LIO）精度下降甚至发散。为此，我们提出了BIEVR-LIO，一种专门设计用于利用可用几何中细微变化以提高鲁棒性的新方法。我们提出了一种高分辨率地图表示，将表面存储为紧凑的体素-wise定向高度图像。这种表示可以直接用于配准，而无需计算中间几何原始体，同时仍支持高效的更新。由于信息几何往往在环境中稀疏分布，我们进一步提出了一种基于地图的点采样策略，以将配准集中在几何信息丰富的区域，从而在信息不足的环境中提高鲁棒性，同时相比全局高分辨率采样减少计算成本。在多个传感器、平台和环境中的实验表明，在约束良好的场景中实现了最先进的性能，在基线方法发散的挑战性场景中实现了显著改进。此外，我们证明了BIEVR-LIO捕获的细粒度几何可以用于下游任务，如机器人运动的地形制图。
英文摘要：
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 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 demonstrate 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
