DPWM: Autonomous Exploration Via Diffusion-Based Map Prediction Guided Planning

Abstract

Autonomous exploration aims to efficiently map unknown environments, yet utilizing limited environmental information to achieve efficient path planning remains challenging. In this work, we focus on leveraging latent information in partial observations to predict the complete environmental structure, thereby furnishing a proposed path planner with the necessary context to devise a long-term optimal exploration strategy. Most existing prediction approaches extract environment features through convolutional neural networks (CNN) and infer the characteristics of neighboring regions. This information then feeds into a value function that evaluates candidate frontiers and guides the robot's planning. Notwithstanding its advantages over traditional heuristic methods, this paradigm remains inherently constrained by its lack of long-term foresight. To this end, we propose dPWM, a diffusion model-based framework for global map prediction, consisting of two key components. The first employs a DDPM with a variable mask to estimate the probability distribution of unknown regions and thereby predict structural features of the global map. We incorporate Gaussian heatmap positional fields into the denoising process via a cross-attention mechanism to enhance regional awareness. This guides the model to focus on nearby areas that are most valuable for exploration. Once the global predictive map is obtained, the second component refers to a designed Watchman Route Problem (WRP) solver to generate an optimal path from the current exploration state. Extensive evaluations show that dPWM reduces exploration path length by 18.53% on HouseExpo and achieves a 16.37% improvement in cross-domain generalization on Dungeon over SOTA baselines. Real-world experiments further validate its effectiveness in physical environments.