# Feature Space Exploration For Planning Initial Benthic AUV Surveys

arXiv: https://arxiv.org/abs/2105.11598 · HTML: https://mlsanity.com/paper/2105.11598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 10 of 10)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Deep Active Learning over the Long Tail](https://mlsanity.com/paper/1711.00941/) (2017)
- [Efficient Informative Sensing using Multiple Robots](https://mlsanity.com/paper/1401.3462/) (2014)
- [The Next Best Underwater View](https://mlsanity.com/paper/1512.01789/) (2015)
- [Multi-Modal Active Perception for Information Gathering in Science Missions](https://mlsanity.com/paper/1712.09716/) (2017)
- [Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel](https://mlsanity.com/paper/1603.06324/) (2016)
- [Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring](https://mlsanity.com/paper/1607.01883/) (2016)
- [Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model](https://mlsanity.com/paper/1908.04537/) (2019)
- [Towards Adaptive Benthic Habitat Mapping](https://mlsanity.com/paper/2006.11453/) (2020)
- [Non-myopic Planetary Exploration Combining In Situ and Remote Measurements](https://mlsanity.com/paper/1904.12255/) (2019)

## Abstract (arXiv)

Special-purpose Autonomous Underwater Vehicles (AUVs) are utilised for benthic (seafloor) surveys, where the vehicle collects optical imagery of the seafloor. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. Therefore it is necessary for AUV paths to sample the surveys areas sparsely, yet effectively. Broad-scale acoustic bathymetric data is readily available over large areas, and is often a useful prior of seafloor cover. As such, prior bathymetry can be used to guide AUV data collection. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry, in order to sample from a diverse set of bathymetric terrain. This will enable the AUV to visit areas that likely contain unique habitats and are representative of the entire survey site. We propose several information gathering planners that utilise a feature space exploration reward, to plan freeform paths or to optimise the placement of a survey template. The suitability of these methods to plan AUV surveys is evaluated based on the coverage of the feature space and also the ability to visit all classes of benthic habitat on the initial dive. Informative planners based on Rapidly-expanding Random Trees (RRT) and Monte-Carlo Tree Search (MCTS) were found to be the most effective. This is a valuable tool for AUV surveys as it increases the utility of initial dives. It also delivers a comprehensive training set to learn a relationship between acoustic bathymetry and visually-derived seafloor classifications.