HK-MEMS, a multi-sensor data set with MEMS LiDAR on degenerate and dynamic urban scenarios

Abstract

Public data sets are essential for progress in autonomous robotics. The advancements in sensor technology and evolving application scenarios create ongoing demands for updated benchmarking resources. This paper introduces the HK-MEMS Data set, the first public data set to provide automotive-grade MEMS LiDAR (Micro-Electromechanical Systems Light Detection and Ranging) data in complex urban environments. While MEMS LiDAR has emerged as a cost-effective and durable alternative to mechanical LiDAR for autonomous vehicles, the lack of data sets hinders corresponding research. Our work targets the under-explored robustness challenge of Simultaneous Localization and Mapping (SLAM) in degenerate and dynamic urban scenarios. The data set integrates multi-modal sensors–including MEMS LiDAR, stereo cameras, GNSS, and an Inertial Navigation System (INS)–collected across three platforms: a handheld device, a mobile robot, and public buses exhibiting real-world driving behaviors. Over 187 min (75.4 km) of data were captured, spanning diverse urban scenarios with dynamic objects and degenerate scenarios, such as tunnels, highways, shopping areas, and subway stations. Comprehensive evaluations of this benchmark's state-of-the-art SLAM algorithms reveal significant performance degradation in degenerate and dynamic scenes, highlighting unresolved challenges in real-world deployment. The HK-MEMS Data set provides a comprehensive resource for evaluating emerging MEMS LiDAR technology and establishes a challenging benchmark for advancing robust SLAM methodologies in urban navigation. The data set is openly available at: https://github.com/RuanJY/HK_MEMS_Dataset.