# Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments

arXiv: https://arxiv.org/abs/2103.11625 · HTML: https://mlsanity.com/paper/2103.11625/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments](https://mlsanity.com/paper/2310.10863/) (2023)

## Abstract (arXiv)

Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions -- a method for efficient distributed sensor planning -- applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.