# Autonomous Exploration and General Visual Inspection of Ship Ballast Water Tanks using Aerial Robots

arXiv: https://arxiv.org/abs/2311.03838 · HTML: https://mlsanity.com/paper/2311.03838/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [SpotlessGS: Relightable 3D Gaussian Splatting under Dynamic Illumination for Robotic Perception](https://mlsanity.com/paper/2608.14713/) (2026)

## Abstract (arXiv)

This paper presents a solution for the autonomous exploration and inspection of Ballast Water Tanks (BWTs) of marine vessels using aerial robots. Ballast tank compartments are critical for a vessel's safety and correspond to confined environments often connected through particularly narrow manholes. The method enables their volumetric exploration combined with visual inspection subject to constraints regarding the viewing distance from a surface. We present evaluation studies in simulation, in a mission consisting of 18 BWT compartments, and in 3 field experiments inside real vessels. The data from one of the experiments is also post-processed to generate semantically-segmented meshes of inspection-important geometries. Geometric models can be associated with onboard camera images for detailed and intuitive analysis.