# Semantics-aware Exploration and Inspection Path Planning

arXiv: https://arxiv.org/abs/2303.07236 · HTML: https://mlsanity.com/paper/2303.07236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Language-Guided Object Search in Agricultural Environments](https://mlsanity.com/paper/2503.01068/) (2025)
- [Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection](https://mlsanity.com/paper/2509.17877/) (2025)
- [SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling](https://mlsanity.com/paper/2608.29315/) (2026)

## Abstract (arXiv)

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that together ensure that a complete mesh model is reconstructed for each semantic, while its surfaces are observed at appropriate resolution and through suitable viewing angles. Evaluated in extensive simulation studies and experimental results using a flying robot, the planner delivers efficient combined exploration and high-fidelity inspection planning that is focused on the semantics of interest. Comparisons against relevant methods of the state-of-the-art are further presented.