Agile locomotion in legged robots poses significant challenges for visual perception. Traditional frame-based cameras often fail in these scenarios for producing blurred images, particularly under low-light conditions. In contrast, event cameras capture changes in brightness asynchronously, offering low latency, high temporal resolution, and high dynamic range. These advantages make them suitable for robust perception during rapid motion and under challenging illumination. However, existing event camera datasets exhibit limitations in stereo configurations and multi-band sensing domains under various illumination conditions. To address this gap, we present M-SEVIQ, a multi-band stereo event visual and inertial quadruped dataset collected using a Unitree Go2 equipped with stereo event cameras, a frame-based camera, an inertial measurement unit (IMU), and joint encoders. This dataset contains more than 30 real-world sequences captured across different velocity levels, illumination wavelengths, and lighting conditions. In addition, comprehensive calibration data, including intrinsic, extrinsic, and temporal alignments, are provided to facilitate accurate sensor fusion and benchmarking. Our M-SEVIQ can be used to support research in agile robot perception, sensor fusion, semantic segmentation and multi-modal vision in challenging environments.

翻译：腿式机器人的敏捷运动对视觉感知提出了重大挑战。在这些场景中，传统基于帧的相机常因产生模糊图像而失效，尤其在低光照条件下。相比之下，事件相机异步捕捉亮度变化，具有低延迟、高时间分辨率和高动态范围的优势。这些优点使其适用于快速运动和挑战性光照条件下的鲁棒感知。然而，现有的事件相机数据集在不同光照条件下的立体配置和多波段感知领域存在局限性。为弥补这一空白，我们提出了M-SEVIQ，这是一个多波段立体事件视觉与惯性四足机器人数据集，使用搭载立体事件相机、一台基于帧的相机、一个惯性测量单元（IMU）以及关节编码器的Unitree Go2机器人采集。该数据集包含超过30个在真实世界中捕获的序列，涵盖了不同速度等级、照明波长和光照条件。此外，提供了全面的校准数据，包括内参、外参和时间对齐，以促进精确的传感器融合与基准测试。我们的M-SEVIQ可用于支持挑战性环境下的敏捷机器人感知、传感器融合、语义分割和多模态视觉研究。