# Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learning

arXiv: https://arxiv.org/abs/2210.02305 · HTML: https://mlsanity.com/paper/2210.02305/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2022.

## Built on by (most cited first, 7 of 7)

- [FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events](https://mlsanity.com/paper/2211.12244/) (2022)
- [EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss](https://mlsanity.com/paper/2305.07853/) (2023)
- [Spike-EVPR: Deep Spiking Residual Networks with SNN-Tailored Representations for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2402.10476/) (2024)
- [Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.15837/) (2024)
- [EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency](https://mlsanity.com/paper/2408.05452/) (2024)
- [Nonlinear Motion-Guided and Spatio-Temporal Aware Network for Unsupervised Event-Based Optical Flow](https://mlsanity.com/paper/2505.05089/) (2025)
- [SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks](https://mlsanity.com/paper/2512.07266/) (2025)

## Abstract (arXiv)

Traditional visual navigation methods of micro aerial vehicle (MAV) usually calculate a passable path that satisfies the constraints depending on a prior map. However, these methods have issues such as high demand for computing resources and poor robustness in face of unfamiliar environments. Aiming to solve the above problems, we propose a neuromorphic reinforcement learning method (Neuro-Planner) that combines spiking neural network (SNN) and deep reinforcement learning (DRL) to realize MAV 3D visual navigation with depth camera. Specifically, we design spiking actor network based on two-state LIF (TS-LIF) neurons and its encoding-decoding schemes for efficient inference. Then our improved hybrid deep deterministic policy gradient (HDDPG) and TS-LIF-based spatio-temporal back propagation (STBP) algorithms are used as the training framework for actor-critic network architecture. To verify the effectiveness of the proposed Neuro-Planner, we carry out detailed comparison experiments with various SNN training algorithm (STBP, BPTT and SLAYER) in the software-in-the-loop (SITL) simulation framework. The navigation success rate of our HDDPG-STBP is 4.3% and 5.3% higher than that of the original DDPG in the two evaluation environments. To the best of our knowledge, this is the first work combining neuromorphic computing and deep reinforcement learning for MAV 3D visual navigation task.