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/.

翻译：利用多传感器可增强复杂环境感知能力，并提升对不同光照条件及高速运动模式的鲁棒性，从而实现精确定位与建图。本文提出ECMD——一个以事件为中心的多感官数据集，包含81个序列，覆盖超过200公里的多种具有挑战性的驾驶场景，包括高速运动、重复场景、动态物体等。ECMD提供两套不同分辨率（640*480、346*260）的立体事件相机、立体工业相机、红外相机、顶部安装的机械式激光雷达（另配两台倾斜激光雷达）、两台消费级GNSS接收器以及车载IMU的数据。同时，车辆的真实位姿通过厘米级高精度GNSS-RTK/INS导航系统获取。所有传感器均在硬件层面完成精确标定与时间同步，并同步记录数据。我们进一步评估了多种先进SLAM算法，以建立视觉与激光雷达SLAM的基准性能，并揭示其局限性。该数据集发布于https://arclab-hku.github.io/ecmd/。