Lidar Variability: A Novel Dataset and Comparative Study of Solid-State and Spinning Lidars

TL;DR

This paper introduces a comprehensive dataset with diverse lidar types, including solid-state and spinning lidars, and evaluates SLAM algorithms and registration techniques to facilitate research in heterogeneous lidar applications.

Contribution

The paper presents the first dataset combining dome-shaped Mid-360 lidars with other solid-state and spinning lidars, and provides benchmark evaluations of SLAM and registration methods across these sensors.

Findings

Diverse lidar data enables cross-platform SLAM evaluation.

Point cloud registration methods vary in accuracy across lidar types.

Benchmark results highlight strengths and limitations of current SLAM algorithms.

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.