Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception (2504.19253v1)

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.

Quantitative Evaluation of Brain-Inspired Vision Sensors in High-Speed Robotic Perception

The paper presents a detailed framework for evaluating brain-inspired vision sensors (BVS), specifically under conditions of high-speed robotic perception. There is a compelling need to address the limitations posed by traditional cameras in dynamic environments, where phenomena such as motion blur can severely impact spatial feature acquisition and subsequent task performance. The introduction of brain-inspired sensors provides an alternate solution, where technologies like event-based vision sensors (EVS) and the primitive-based Tianmouc sensor are gaining prominence due to their high temporal resolution paired with reduced bandwidth and power demands.

Evaluation Methodology

The authors have developed a robust quantitative evaluation framework that tests two major classes of BVS: EVS, which detect asynchronous temporal contrasts, and Tianmouc, which uses a complementary mechanism to encode both spatiotemporal variations and intensity. Their framework offers a unified testing protocol with cross-sensor calibrations, standardized testing environments, and suitable quality metrics to effectively compare and contrast results across different sensor modalities. Specific challenges of BVS are explored, such as motion-induced distortion affecting structural information capture and impacts on tasks like corner detection and motion estimation at varied rotational speeds.

Sensor Performance and Results

The experimentation reveals several insights into the performance of these sensors under different conditions:

1. Event-based Vision Sensors (EVS): EVS exhibited competence in high-speed, sparse scenes and performed reasonably well in moderate-speed, complex environments. However, they showed limitations in high-speed, cluttered scenes due to inconsistencies in pixel-level bandwidth and event rate saturation.
2. Primitive-based Sensor Tianmouc: This sensor demonstrated consistent performance across all tested conditions - from low to high speeds in sparse and complex environments. The robustness of Tianmouc stems from its capability to perform global, precise high-speed spatiotemporal gradient sampling, which essentially mitigates the challenges encountered by traditional and event-based sensors.

Implications

The paper provides valuable insight into the suitability of BVS for various application scenarios. Practically, this means that while EVS might be suited for scenarios with limited complexity and modest speed demands, applications requiring high-speed perception amidst complexity would benefit from utilizing sensors like Tianmouc. Theoretical implications point to advancing sensor design and functionality in AI—particularly in enhancing perception under dynamic and ambiguous conditions. The comprehensive analysis also highlights the importance of continuous advancements in event-based algorithm efficiency and capabilities to further expand sensor applicability.

Future Directions

The direction for future research involves expanding these preliminary evaluations to broader robotic tasks, including those involving visual odometry, and assessing BVS performance under highly dynamic range environments. Additionally, further advancements in processing algorithms and software development are anticipated to maximize the operational potential of emerging technologies like Tianmouc, while acknowledging the evolutionary nature of event camera technologies.

This paper thus sets the groundwork for understanding and leveraging the potential of brain-inspired vision sensors, emphasizing both the challenges and opportunities they present for enriched robotic perception in high-speed contexts.