Zhang Sen, Zha Fusheng, Wang Xiangji, Li Mantian, Guo Wei, Wang Pengfei, Li Xiaolin, Sun Lining
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.
Lanzhou University of Technology, Lanzhou, China.
Front Neurorobot. 2025 Mar 12;19:1537673. doi: 10.3389/fnbot.2025.1537673. eCollection 2025.
Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in various robotic applications. However, despite their inherent sparsity, most existing visual processing algorithms are optimized for conventional standard cameras and dense images captured from them, resulting in computational redundancy and high latency when applied to event-based cameras. To address this gap, we propose a sparse convolution operator tailored for event-based cameras. By selectively skipping invalid sub-convolutions and efficiently reorganizing valid computations, our operator reduces computational workload by nearly 90% and achieves almost 2× acceleration in processing speed, while maintaining the same accuracy as dense convolution operators. This innovation unlocks the potential of event-based cameras in applications such as autonomous navigation, real-time object tracking, and industrial inspection, enabling low-latency and high-efficiency perception in resource-constrained robotic systems.
基于事件的相机是受生物启发的视觉传感器，它模仿动物视网膜的稀疏和异步激活，在各种机器人应用中具有低延迟和低计算量等优点。然而，尽管其具有固有的稀疏性，但大多数现有的视觉处理算法是针对传统标准相机及其捕获的密集图像进行优化的，应用于基于事件的相机时会导致计算冗余和高延迟。为了弥补这一差距，我们提出了一种专门为基于事件的相机量身定制的稀疏卷积算子。通过有选择地跳过无效的子卷积并有效地重组有效计算，我们的算子将计算工作量减少了近90%，处理速度提高了近两倍，同时保持与密集卷积算子相同的精度。这一创新释放了基于事件的相机在自主导航、实时目标跟踪和工业检测等应用中的潜力，能够在资源受限的机器人系统中实现低延迟和高效感知。
