构建首个覆盖多样退化场景的多传感器激光雷达数据集，助力鲁棒定位算法评测。
Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios
- 设计多传感器融合数据集，覆盖7类退化环境
- 包含64条轨迹超64公里，支持算法全面评估
- 适合自动驾驶与机器人定位研究者使用
基于3D激光雷达的位姿估计与地图构建显著提升机器人自主性。然而，现有开源数据集缺乏几何退化环境的表征，限制了鲁棒激光雷达SLAM算法的发展与评测。为填补这一空白，我们提出GEODE——一个涵盖多激光雷达、多场景的综合性数据集，专为真实世界中的几何退化环境设计。GEODE包含64条轨迹，总长度超过64公里，覆盖7种不同环境，退化程度各异。数据采集过程精细，整合多种激光雷达传感器、双目相机、惯性测量单元（IMU）及多样化运动条件，旨在推动通用算法的发展。我们利用GEODE评估当前主流SLAM方法，揭示其在退化场景下的局限性。该数据集将公开发布于https://geode.github.io，助力激光雷达SLAM技术持续进步。
原文摘要 · Abstract (English)
The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarking of robust LiDAR SLAM algorithms. To address this gap, we introduce GEODE, a comprehensive multi-LiDAR, multi-scenario dataset specifically designed to include real-world geometrically degenerate environments. GEODE comprises 64 trajectories spanning over 64 kilometers across seven diverse settings with varying degrees of degeneracy. The data was meticulously collected to promote the development of versatile algorithms by incorporating various LiDAR sensors, stereo cameras, IMUs, and diverse motion conditions. We evaluate state-of-the-art SLAM approaches using the GEODE dataset to highlight current limitations in LiDAR SLAM techniques. This extensive dataset will be publicly available at https://geode.github.io, supporting further advancements in LiDAR-based SLAM.
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