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Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios

Zhiqiang Chen, Yuhua Qi, Dapeng Feng, Xuebin Zhuang, Hongbo Chen, Xiangcheng Hu, Jin Wu, Kelin Peng, Peng Lu

Abstract

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.

Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios

Abstract

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.
Paper Structure (47 sections, 9 figures, 6 tables)

This paper contains 47 sections, 9 figures, 6 tables.

Table of Contents

  1. INTRODUCTION
  2. Motivation
  3. Contributions
  4. Organization
  5. RELATED WORK
  6. SYSTEM OVERVIEW
  7. Sensors Setup
  8. Time Synchronization Scheme
  9. Sensor Calibration
  10. IMU Calibration:
  11. Stereo Cameras Calibration:
  12. Camera-IMU Extrinsic Calibration:
  13. Camera-LiDAR Extrinsic Calibration:
  14. LiDAR-IMU Extrinsic Calibration:
  15. DATASET
  16. ...and 32 more sections

Figures (9)

  • Figure 1: Multi-Sensor Devices and Data Collection Platforms. (a) SolidWorks models of our sensor rig on three data collection devices, with the coordinate axes color-coded: red for the $X$-axis, green for the $Y$-axis, and blue for the $Z$-axis. This representation illustrates the transformation of sensor coordinates for each device. The multi-sensor rig mounted on (b) a handheld platform, (c) a sailboat, and (d) an UGV. The images in (b) through (d) demonstrate the diverse range of the GEODE dataset across various data collection platforms.
  • Figure 2: Time synchronization scheme.
  • Figure 3: Images from different scenes.
  • Figure 4: Visualization of multi-degenerate scenarios in LiDAR point cloud data: representation of spatial map and current frame degradations. The translational degradation is denoted by the orange arrow $\leftrightarrow$, while the rotational degradation is signified by the purple arrow $\rightarrow$.
  • Figure 5: Trajectories of multiple sequences captured in diverse outdoor scenarios, encompassing environments with varying scales and degrees of degradation, such as off-road areas, inland waterways, urban tunnels, and bridges.
  • ...and 4 more figures