标题： 高速机器人感知中类脑视觉传感器的定量评估 显示英文标题

标题： Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception

摘要： 在机器人系统中，当依赖传统相机时，在高速和动态条件下感知系统会遇到重大挑战，因为在这些情况下运动模糊可能会损害空间特征的完整性并影响任务表现。 受大脑启发的视觉传感器（BVS）最近作为一种替代方案引起了关注，它提供了高时间分辨率的同时减少了带宽和功耗需求。 在这里，我们提出了第一个针对变速机器人传感中两种代表性BVS类别的定量评估框架，其中包括检测异步时间对比度的事件驱动视觉传感器（EVS），以及采用互补机制来编码时空变化和强度的基于原始信号的传感器Tianmouc。 建立了一个统一的测试协议，包括跨传感器校准、标准化测试平台和质量指标以解决数据模态差异问题。 从成像的角度来看，我们评估了传感器非理想特性（如运动引起的失真）对捕捉结构信息的影响。 对于功能基准测试，我们在不同旋转速度下检查角点检测和运动估计的任务表现。 结果显示，EVS在高速稀疏场景和适度快速复杂场景中表现出色，但在高速混乱环境中由于像素级带宽变化和事件率饱和而表现出性能限制。 相比之下，Tianmouc在其全球、精确、高速时空梯度采样的支持下，在各种速度下的稀疏和复杂场景中表现出一致的性能。 这些发现为BVS技术的应用相关适用性提供了宝贵的见解，并支持该领域的进一步发展。 显示英文摘要

摘要： Perception systems in robotics encounter significant challenges in high-speed and dynamic conditions when relying on traditional cameras, where motion blur can compromise spatial feature integrity and task performance. Brain-inspired vision sensors (BVS) have recently gained attention as an alternative, offering high temporal resolution with reduced bandwidth and power requirements. Here, we present the first quantitative evaluation framework for two representative classes of BVSs in variable-speed robotic sensing, including event-based vision sensors (EVS) that detect asynchronous temporal contrasts, and the primitive-based sensor Tianmouc that employs a complementary mechanism to encode both spatiotemporal changes and intensity. A unified testing protocol is established, including crosssensor calibrations, standardized testing platforms, and quality metrics to address differences in data modality. From an imaging standpoint, we evaluate the effects of sensor non-idealities, such as motion-induced distortion, on the capture of structural information. For functional benchmarking, we examine task performance in corner detection and motion estimation under different rotational speeds. Results indicate that EVS performs well in highspeed, sparse scenarios and in modestly fast, complex scenes, but exhibits performance limitations in high-speed, cluttered settings due to pixel-level bandwidth variations and event rate saturation. In comparison, Tianmouc demonstrates consistent performance across sparse and complex scenarios at various speeds, supported by its global, precise, high-speed spatiotemporal gradient samplings. These findings offer valuable insights into the applicationdependent suitability of BVS technologies and support further advancement in this area.