WOLF: World Model Guided LiDAR Exploration with Predictive Frontiers

2026-09-20

AI Summary

该研究提出WOLF框架，通过预测未来观测来改进基于LiDAR的无人机探索，使用循环世界模型学习观测动态，并结合预测前沿机制指导进一步感知。

Abstract

LiDAR-based unmanned aerial vehicle (UAV) exploration builds maps by continually selecting where to observe next. However, decisions based on the measured map provide limited foresight into spatial continuations behind occlusions, leaving potentially informative directions unrecognized. We present WOLF, a world-model-guided framework that predicts future observations to enhance autonomous exploration. In the training stage, a recurrent world model learns observation dynamics from exploration trajectories, with recurrent memory retaining the spatial context needed to interpret partial observations across successive views. Building on this context, the model combines observation history with candidate motions during exploration to predict local occupancy and visibility. To guide further sensing, a predictive frontier generation mechanism then aligns and fuses these predictions using confidence, branch agreement, and observation quality to identify promising regions. The resulting predictive frontiers join measured ones to guide geometric viewpoint selection and trajectory generation, while new scans update subsequent predictions. In simulations, our method reduces mean terminal time by 10.9% relative to EPIC in Garage at comparable coverage and increases mean coverage from 42.12% to 98.35% in Tunnel. Real-world experiments further demonstrate onboard deployment of the learned model for online inference during physical flight.

Problem

• LiDAR
• UAV exploration
• spatial continuations
• occlusions
• autonomous exploration

Innovation

• recurrent world model
• predictive frontier generation
• autonomous exploration