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

arXiv: https://arxiv.org/abs/2608.29315 · HTML: https://mlsanity.com/paper/2608.29315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Autonomous Exploration Development Environment and the Planning Algorithms](https://mlsanity.com/paper/2110.14573/) (2021)
- [Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration](https://mlsanity.com/paper/2112.04063/) (2021)
- [Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments](https://mlsanity.com/paper/2104.03724/) (2021)
- [Semantics-aware Exploration and Inspection Path Planning](https://mlsanity.com/paper/2303.07236/) (2023)
- [STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments](https://mlsanity.com/paper/2402.02566/) (2024)
- [An Integrated Approach to Goal Selection in Mobile Robot Exploration](https://mlsanity.com/paper/2007.10085/) (2020)
- [Rhino: An Autonomous Robot for Mapping Underground Mine Environments](https://mlsanity.com/paper/2305.06958/) (2023)
- [Topological Exploration using Segmented Map with Keyframe Contribution in Subterranean Environments](https://mlsanity.com/paper/2309.08397/) (2023)
- [Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis](https://mlsanity.com/paper/2409.15671/) (2024)
- [SeGuE: Semantic Guided Exploration for Mobile Robots](https://mlsanity.com/paper/2504.03629/) (2025)

## Abstract (arXiv)

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.