WECMD: A multisensor dataset for wearable event cameras in the age of embodied intelligence

In the era of embodied intelligence, wearable datasets for pedestrian navigation are essential. However, publicly available multisensory datasets tailored to such scenarios remain scarce. Traditional sensors such as RGB cameras and LiDAR often struggle to capture the fast and irregular dynamics of human motion. To address this gap, we introduce a large-scale pedestrian-wearable dataset primarily recorded using event cameras. The dataset includes event camera, RGB cameras, LiDAR sensors, a tactical-grade IMU, and GNSS, covering a wide range of indoor and outdoor environments with diverse motion types and illumination conditions. High-precision ground truth is obtained using motion capture systems indoors and GNSS/IMU integration with bidirectional smoothing outdoors. The dataset is structured into 23 subsets categorized by motion dynamics and lighting, supporting the development and evaluation of robust localization and SLAM algorithms. Benchmarking with state-of-the-art frameworks reveals notable performance degradation under highly dynamic or low-light conditions, highlighting the dataset’s value for advancing pedestrian navigation and event-based perception. The dataset and tools are publicly available at: https://github.com/xueli-guo/WECMD.git . • A large-scale pedestrian-wearable event camera dataset (WECMD) is introduced , supporting embodied intelligence and SLAM research in dynamic, human-centered environments with full pose ground truth under realistic motion scenarios. • The dataset integrates multisensor modalities — including event/RGB cameras, solid-state and mechanical LiDARs, IMU, and GNSS — collected in diverse lighting conditions (normal, low-light, dark) and motion types (walking, stair-climbing, running, UGV, SUV). • A time-synchronized sensor calibration and fusion framework is developed , enabling accurate multimodal data alignment for high-speed motion perception and pedestrian navigation applications. • Benchmark evaluations of state-of-the-art SLAM algorithms are conducted , exposing performance limitations under fast dynamics and low-light scenarios, and providing a new standard for event-based perception benchmarking. • WECMD fills a critical gap in the community , offering the first open dataset to support event-based, wearable, and human-oriented navigation algorithm development across both indoor and outdoor domains.