Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception
Journal:
arXiv
Published Date:
Apr 27, 2025
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.
