# Informative Path Planning for Active Field Mapping under Localization Uncertainty

arXiv: https://arxiv.org/abs/1902.09660 · HTML: https://mlsanity.com/paper/1902.09660/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Peering into the Unknown: Active View Selection with Neural Uncertainty Maps for 3D Reconstruction](https://mlsanity.com/paper/2506.14856/) (2025)
- [Reinforcement Learning for Agile Active Target Sensing with a UAV](https://mlsanity.com/paper/2212.08214/) (2022)
- [Constrained Bayesian Experimental Design via Online Planning](https://mlsanity.com/paper/2605.26990/) (2026)
- [RUMI: Rummaging Using Mutual Information](https://mlsanity.com/paper/2408.10450/) (2024)
- [Combining Deep Architectures for Information Gain estimation and Reinforcement Learning for multiagent field exploration](https://mlsanity.com/paper/2505.23865/) (2025)
- [Attention-based Learning for 3D Informative Path Planning](https://mlsanity.com/paper/2506.08434/) (2025)

## Abstract (arXiv)

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.