# 迈向生物启发视觉导航：面向 MAVs 的神经形态导航范式

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-21978
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：提出端到端事件驱动神经形态范式，结合事件视觉、SNN与处理器，在仿真和真实部署中验证MAV复杂环境视觉导航。

# 迈向生物启发视觉导航：面向 MAVs 的神经形态导航范式

::: v-pre
> 原文链接：[Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs](https://arxiv.org/abs/2610.21978)（arXiv 2610.21978 · 发布日期：2026-10-15）

**Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs**

提出端到端事件驱动神经形态范式，结合事件视觉、SNN与处理器，在仿真和真实部署中验证MAV复杂环境视觉导航。

## 核心要点

- 面向复杂环境的能耗受限MAV，基于帧和ANN的导航在鲁棒性与能效上受限。
- 方法从事件流提取环境表征，经脉冲导航模块用引导学习和群体编码学习策略并输出航点。
- 实验显示该神经形态方法的导航性能与ANN方法相当。
- 感知模块功耗为ANN的19.57%，Loihi上导航模块为ANN对应方案的12.37%。

**验证范围与局限**：验证范围为复杂环境中的MAV视觉导航，覆盖仿真与真实世界部署。

## 英文摘要 (Abstract)

Biological visual navigation achieves remarkable robustness at extremely low power, whereas frame-based robotic systems relying on artificial neural networks remain limited in both respects, particularly for energy-constrained micro aerial vehicles (MAVs) operating in complex environments. Inspired by the event-driven, sparse, and asynchronous principles of biological visual systems, neuromorphic sensing and computation provide a promising alternative for robust and energy-efficient navigation. In this paper, we propose the first end-to-end event-driven neuromorphic paradigm for MAV visual navigation in complex environments by integrating neuromorphic vision, spiking neural networks, and neuromorphic processors. The proposed method first extracts the environmental representation from event streams using a spiking perceptual representation module. This representation is fed into a spiking navigation module, where guidance learning and population coding enable efficient policy learning with few time steps. The navigation module then outputs a waypoint converted into executable trajectory points. Experiments show that the proposed neuromorphic method achieves navigation performance comparable to ANN-based methods while markedly reducing power consumption. The spiking perceptual module consumes only 19.57% of the power of its ANN counterpart, while the spiking navigation module deployed on Intel Loihi consumes only 12.37% of that required by its ANN counterpart running on NVIDIA Orin NX. Beyond prior neuromorphic systems focused mainly on relatively simple navigation subtasks, we demonstrate a complete neuromorphic visual navigation system spanning simulation and real-world deployment. Our results establish the feasibility of the neuromorphic paradigm for autonomous navigation and provide a bio-inspired pathway toward robust and low-power MAV navigation.

作者：Junjie Jiang, Ankang Zhou, Zheng Fang
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## 原始参考来源 (Sources)

- [arXiv 2610.21978：Towards bio-inspired visual navigation: a neuromorphic navigation paradigm for MAVs](https://arxiv.org/abs/2610.21978)