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