# A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments under Severe Odometry Drift

arXiv: https://arxiv.org/abs/2010.09859 · HTML: https://mlsanity.com/paper/2010.09859/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Describe Anything Anywhere At Any Moment](https://mlsanity.com/paper/2512.00565/) (2025)
- [FrontierNet: Learning Visual Cues to Explore](https://mlsanity.com/paper/2501.04597/) (2025)
- [FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment](https://mlsanity.com/paper/2504.08603/) (2025)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching](https://mlsanity.com/paper/2404.01674/) (2024)
- [OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments](https://mlsanity.com/paper/2406.07294/) (2024)
- [Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates](https://mlsanity.com/paper/2409.15493/) (2024)
- [NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot](https://mlsanity.com/paper/2410.11492/) (2024)

## Abstract (arXiv)

Exploration is a fundamental problem in robot autonomy. A major limitation, however, is that during exploration robots oftentimes have to rely on on-board systems alone for state estimation, accumulating significant drift over time in large environments. Drift can be detrimental to robot safety and exploration performance. In this work, a submap-based, multi-layer approach for both mapping and planning is proposed to enable safe and efficient volumetric exploration of large scale environments despite odometry drift. The central idea of our approach combines local (temporally and spatially) and global mapping to guarantee safety and efficiency. Similarly, our planning approach leverages the presented map to compute global volumetric frontiers in a changing global map and utilizes the nature of exploration dealing with partial information for efficient local and global planning. The presented system is thoroughly evaluated and shown to outperform state of the art methods even under drift-free conditions. Our system, termed GLoca}, will be made available open source.