Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception
🤖 AI Summary
To address the degradation of perception performance in high-speed dynamic scenarios caused by motion blur in conventional frame-based cameras, this paper establishes, for the first time, a quantitative evaluation framework for brain-inspired visual sensors tailored to robotic high-speed perception. We systematically compare event-based cameras (EVS) and the Tianmouc sensor across multi-velocity dynamic scenes in terms of imaging quality and task-level performance. Our method introduces a unified cross-sensor calibration protocol, a standardized benchmarking platform, and a suite of multidimensional quality metrics. Experimental results reveal a bandwidth saturation bottleneck in EVS under high-speed motion, whereas Tianmouc—leveraging global, high-precision spatiotemporal gradient sampling—demonstrates superior and more stable robustness in corner detection, optical flow estimation, and other standard benchmarks. This work provides reproducible, quantitative foundations for sensor selection and optimization in brain-inspired vision systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Evaluates brain-inspired vision sensors for high-speed robotic perception challenges
Compares event-based and primitive-based sensors in dynamic conditions
Assesses sensor performance in motion blur and bandwidth limitations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantitative evaluation framework for brain-inspired vision sensors
Unified testing protocol with cross-sensor calibrations
Global precise high-speed spatiotemporal gradient samplings
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