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Leveraging multiple sensors enhances complex environmental perception andincreases resilience to varying luminance conditions and high-speed motionpatterns, achieving precise localization and mapping. This paper proposes,ECMD, an event-centric multisensory dataset containing 81 sequences andcovering over 200 km of various challenging driving scenarios includinghigh-speed motion, repetitive scenarios, dynamic objects, etc. ECMD providesdata from two sets of stereo event cameras with different resolutions (640*480,346*260), stereo industrial cameras, an infrared camera, a top-installedmechanical LiDAR with two slanted LiDARs, two consumer-level GNSS receivers,and an onboard IMU. Meanwhile, the ground-truth of the vehicle was obtainedusing a centimeter-level high-accuracy GNSS-RTK/INS navigation system. Allsensors are well-calibrated and temporally synchronized at the hardware level,with recording data simultaneously. We additionally evaluate severalstate-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM andidentifying their limitations. The dataset is available athttps://arclab-hku.github.io/ecmd/.

ECMD: An Event-Centric Multisensory Driving Dataset for SLAM