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HK-MEMS, a MEMS LiDAR dataset on urban tunnels and dynamic scenarios
  • Jianyuan Ruan,
  • Dan Zhang
Jianyuan Ruan
The Hong Kong Polytechnic University Department of Mechanical Engineering
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Dan Zhang
The Hong Kong Polytechnic University Department of Mechanical Engineering

Corresponding Author:[email protected]

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Abstract

not-yet-known not-yet-known not-yet-known unknown Public datasets play a crucial role in advancing autonomous robotics research. The rapid evolution of sensors and applications continually drives the need for new datasets. For instance, the shift from mechanical LiDAR (Light Detection and Ranging) sensors on autonomous vehicles to (hybrid) solid-state LiDAR technologies like MEMS (Micro-electromechanical systems) LiDAR has brought about enhanced durability and reduced costs. However, datasets supporting research on these sensors are scarce. This paper presents the multi-modular HK-MEMS dataset, incorporating data from LiDARs, a camera, GNSS, and Inertial Navigation Systems. Notably, it is the first dataset to offer automotive-grade MEMS LiDAR data for research in Simultaneous Localization and Mapping (SLAM). Compared with existing datasets, our data emphasize extreme environments like degenerate urban tunnels and dynamic scenarios, aiming to enhance the robustness of SLAM systems. The data are collected on various platforms including a handheld device, a mobile robot, and notably, buses with real driving behaviors. We collect 187 minutes and 75.4 kilometers of data. State-of-the-art SLAM methods are evaluated on this benchmark. The result highlights the challenges in extreme environments and underscores the ongoing need to enhance the robustness of SLAM systems. This dataset serves as a valuable platform for exploring the potential and limitations of MEMS LiDAR, and a challenge to enhance the robustness of SLAM in urban navigation scenarios. The data is available at https://github.com/RuanJY/HK_MEMS_Dataset.
30 Aug 2024Submitted to Journal of Field Robotics
02 Sep 2024Submission Checks Completed
02 Sep 2024Assigned to Editor
02 Sep 2024Review(s) Completed, Editorial Evaluation Pending
29 Sep 2024Reviewer(s) Assigned