A1 Refereed original research article in a scientific journal
Understanding Lidar Variability: A Dataset and Comparative Study Featuring Dome-Shaped, Solid-State, and Spinning Lidars
Authors: Doumegna, Mawuto Koudjo Felix; Yu, Xianjia; Zhang, Jiaqiang; Ha, Sier; Zou, Zhuo; Westerlund, Tomi
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2026
Journal: IEEE Robotics and Automation Letters
Volume: 11
Issue: 1
First page : 570
Last page: 577
ISSN: 2377-3766
eISSN: 2377-3774
DOI: https://doi.org/10.1109/LRA.2025.3632749
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1109/lra.2025.3632749
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/505627950
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.
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