用事件相机+脉冲网络实现低功耗高效场景识别
Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition
- 结合事件相机与脉冲神经网络生成紧凑不变的场景特征
- 在光照、视角变化下表现优异，参数量少50倍，能耗降250倍
- 适合移动机器人和类脑计算平台实时部署
在动态真实环境下实现可靠的视觉场景识别（VPR）对自主机器人至关重要，但传统深度网络受限于高计算与能耗。受哺乳动物导航系统启发，我们提出SpikeVPR，一种生物启发的类脑方法，结合事件相机与脉冲神经网络（SNNs），从少量样本中生成紧凑且不变的场景描述符，在光照、视角、外观剧烈变化下仍保持鲁棒性。SpikeVPR采用代理梯度训练，并引入EventDilation新数据增强策略，提升对速度与时间变化的适应能力。在两个挑战性基准（Brisbane-Event-VPR和NSAVP）上，性能媲美最先进深度网络，参数量减少50倍，能耗分别降低30倍和250倍，支持在移动端与类脑平台实现实时运行。结果表明，基于脉冲编码为复杂变化环境中的鲁棒VPR提供了高效路径。
原文摘要 · Abstract (English)
Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural networks (SNNs) to generate compact, invariant place descriptors from few exemplars, achieving robust recognition under extreme changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Evaluated on two challenging benchmarks (Brisbane-Event-VPR and NSAVP), SpikeVPR achieves performance comparable to state-of-the-art deep networks while using 50 times fewer parameters and consuming 30 and 250 times less energy, enabling real-time deployment on mobile and neuromorphic platforms. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.
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