SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling

2026-08-29

AI Summary

本文提出了一种基于语义引导的探索方法SGE，通过图像空间航点采样解决非结构化环境中的地面车辆导航问题，结合语义分割和路径优化技术。

Abstract

This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization. Unlike conventional geometric exploration methods, SGE evaluates candidate exploration goals directly in the image space using a semantic-aware utility function that accounts for terrain traversability, obstacle proximity, objects of interest, and depth-based exploration reward. Sampled waypoints are projected into 3D and ordered through a real-time Traveling Salesman Problem (TSP) formulation, enabling receding-horizon goal selection. To address real-world navigation uncertainty, the framework introduces mechanisms, including temporary taboo regions to handle navigation failures and a graph-based relocation strategy for efficient backtracking across explored areas. We evaluate SGE in standardized simulation benchmarks against state-of-the-art exploration planners and demonstrate competitive performance in volumetric coverage, while enabling semantic task biasing that cannot be achieved by purely geometric methods. The framework is further validated through real-world experiments using multiple robotic platforms in indoor campus buildings and in limestone and coal mines. Results show consistent performance and adaptability across platforms and domains.

Problem

• exploration
• unstructured environments
• semantic segmentation
• waypoint selection
• route optimization

Innovation

• Semantic Segmentation
• Waypoint Sampling
• Receding-Horizon Optimization
• Traveling Salesman Problem
• Taboo Regions