This paper introduces a novel framework for robotic vision-based navigation that integrates Hybrid Neural Networks (HNNs) with Spiking Neural Network (SNN)-based filtering to enhance situational awareness for unmodeled obstacle detection and localization. By leveraging the complementary strengths of Artificial Neural Networks (ANNs) and SNNs, the system achieves both accurate environmental understanding and fast, energy-efficient processing. The proposed architecture employs a dual-pathway approach: an ANN component processes static spatial features at low frequency, while an SNN component handles dynamic, event-based sensor data in real time. Unlike conventional hybrid architectures that rely on domain conversion mechanisms, our system incorporates a pre-developed SNN-based filter that directly utilizes spike-encoded inputs for localization and state estimation. Detected anomalies are validated using contextual information from the ANN pathway and continuously tracked to support anticipatory navigation strategies. Simulation results demonstrate that the proposed method offers acceptable detection accuracy while maintaining computational efficiency close to SNN-only implementations, which operate at a fraction of the resource cost. This framework represents a significant advancement in neuromorphic navigation systems for robots operating in unpredictable and dynamic environments.

翻译：本文提出了一种新颖的机器人视觉导航框架，该框架将混合神经网络与基于脉冲神经网络的滤波技术相结合，以增强对未建模障碍物的检测与定位的情境感知能力。通过融合人工神经网络和脉冲神经网络的互补优势，该系统既能实现准确的环境理解，又能进行快速、高能效的处理。所提出的架构采用双通路方法：ANN组件以低频处理静态空间特征，而SNN组件则实时处理基于事件的动态传感器数据。与依赖域转换机制的传统混合架构不同，我们的系统集成了一个预先开发的基于SNN的滤波器，该滤波器直接利用脉冲编码输入进行定位与状态估计。检测到的异常通过ANN通路提供的上下文信息进行验证，并持续跟踪以支持预见性导航策略。仿真结果表明，所提方法在保持接近纯SNN实现的计算效率（其资源消耗仅为传统方法的一小部分）的同时，提供了可接受的检测精度。该框架代表了在不可预测和动态环境中运行的机器人神经形态导航系统的重要进展。