Abstract
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.
Abstract (translated)
敏捷腿足机器人在视觉感知方面面临重大挑战。传统的基于帧的摄像头在这种情况下往往表现不佳，特别是在低光条件下会拍摄到模糊图像。相比之下，事件相机以异步方式捕捉亮度变化，提供低延迟、高时间分辨率和宽动态范围的优势。这些优势使它们非常适合快速移动时以及各种照明条件下的稳健感知。然而，现有的事件相机数据集在立体配置和多频带感应领域存在局限性，尤其是在不同的光照条件下。 为解决这一缺口，我们提出了M-SEVIQ，这是一个使用配备立体事件相机、基于帧的摄像头、惯性测量单元（IMU）和关节编码器的Unitree Go2收集的多频带立体事件视觉与惯性四足机器人数据集。该数据集包含超过30个在不同速度等级、光照波长以及照明条件下的真实世界序列。此外，还提供了全面的校准数据，包括固有参数、外在参数和时间对齐信息，以支持精确的传感器融合和基准测试。我们的M-SEVIQ可以用于研究敏捷机器人感知、传感器融合、语义分割及多模态视觉在具有挑战性环境中的应用。
URL
https://arxiv.org/abs/2601.02777
