Understanding Lidar Variability: A Dataset and Comparative Study Featuring Dome-Shaped, Solid-State, and Spinning Lidars
Mawuto Koudjo Felix Doumegna, Xianjia Yu, Jiaqiang Zhang, Sier Ha, Zhuo Zou, Tomi Westerlund
AI summary
Problem
Existing datasets lack synchronized data from dome-shaped, solid-state, and spinning lidars on a single platform, hindering fair comparative evaluation of SLAM algorithms. Additionally, the independent impact of lidar-specific scanning patterns and resolution on point cloud registration remains unclear without IMU assistance.
Approach
The authors developed a novel dataset integrating Livox Avia, Livox Mid-360, and Ouster OS0-128 lidars on a unified robotic platform with high-precision ground truth. They then benchmarked five state-of-the-art SLAM methods and evaluated point-to-point, point-to-plane, and hybrid registration techniques across indoor and outdoor environments.
Key results
- First dataset synchronizing spinning, solid-state, and dome-shaped lidars with motion capture and GNSS ground truth
- Comprehensive benchmark of five state-of-the-art SLAM algorithms across heterogeneous lidar data
- Quantitative analysis of point cloud registration methods in IMU-free contexts
- Established baseline performance metrics for SLAM and 3D reconstruction across diverse lidar platforms
Why it matters
Provides a foundational benchmark for researchers and practitioners evaluating and developing SLAM and 3D reconstruction algorithms across cost-effective and high-end lidar platforms.
Abstract
Lidar technology has been widely employed across various applications, such as robot localization in GNSS-denied environments and 3D reconstruction. Recent advancements have introduced different lidar types, including cost-effective solid- state lidars such as the Livox Avia and Mid-360. The Mid- 360, with its dome-like design, is increasingly used in portable mapping and unmanned aerial vehicle (UAV) applications due to its low cost, compact size, and reliable performance. However, the lack of datasets that include dome-shaped lidars, such as the Mid- 360, alongside other solid-state and spinning lidars significantly hinders the comparative evaluation of novel approaches across platforms. Additionally, performance differences between low- cost solid-state and high-end spinning lidars (e.g., Ouster OS series) remain insufficiently examined, particularly without an Inertial Measurement Unit (IMU) in odometry. To address this gap, we introduce a novel dataset comprising data from multiple lidar types, including the low-cost Livox Avia and the dome-shaped Mid-360, as well as high-end spinning lidars such as the Ouster series. Notably, to the best of our knowledge, no existing dataset comprehensively includes dome- shaped lidars such as Mid-360 alongside both other solid-state and spinning lidars. In addition to the dataset, we provide a benchmark evaluation of state-of-the-art SLAM algorithms applied to this diverse sensor data. Furthermore, we present a quantitative analysis of point cloud registration techniques, specifically point-to-point, point-to-plane, and hybrid methods, using indoor and outdoor data collected from the included lidar systems. The outcomes of this study establish a foundational reference for future research in SLAM and 3D reconstruction across heterogeneous lidar platforms.