# Informative Path Planning for Active Field Mapping under Localization Uncertainty

## TL;DR

This paper presents an informative planning framework for active field mapping that explicitly incorporates robot pose uncertainty using Gaussian Processes, leading to more robust and accurate environmental maps.

## Contribution

It introduces a novel utility function coupling localization and mapping objectives in GP-based planning, addressing pose uncertainty explicitly.

## Key findings

- Reduces mean pose uncertainty in simulations
- Decreases map error compared to existing strategies
- Demonstrates effectiveness in indoor temperature mapping

## Abstract

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.