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

2025-07-06

AI Summary

Existing LiDAR SLAM research lacks a unified benchmark dataset encompassing heterogeneous sensors—including dome-shaped (e.g., Hesai Mid-360), solid-state (e.g., Livox Avia), and rotating (e.g., Ouster OS-series) LiDARs—and the odometry performance differences among these sensor types under IMU-free conditions remain poorly characterized. Method: We introduce the first cross-type, multi-platform synchronized LiDAR dataset, featuring the first systematic integration of dome-shaped LiDARs (e.g., Mid-360) across diverse indoor and outdoor scenarios. Leveraging point-to-point, point-to-plane, and hybrid registration strategies, we quantitatively evaluate mainstream SLAM algorithms in IMU-free settings. Contribution/Results: We establish a comprehensive SLAM and 3D reconstruction benchmark tailored to heterogeneous LiDARs, revealing critical trade-offs—particularly in accuracy, robustness, and real-time performance—between low-cost solid-state and high-end rotating LiDARs. This work fills longstanding gaps in both cross-platform benchmark data and evaluation methodology.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• Lack of datasets comparing dome-shaped and other lidar types
• Unclear performance differences between solid-state and spinning lidars
• Need for benchmark evaluation of SLAM algorithms on diverse lidars

Innovation

• Novel dataset includes diverse lidar types
• Benchmark evaluation of SLAM algorithms
• Quantitative analysis of point cloud registration