Monte Carlo Localization in Hand-Drawn Maps

TL;DR

This paper presents a novel Monte Carlo localization method that enables robots to localize within hand-drawn maps without prior environment maps, by estimating robot pose and map deformation simultaneously, achieving up to 80% robustness.

Contribution

It introduces a new approach for robot localization using hand-drawn maps, addressing the challenge of unknown environments and map inaccuracies.

Findings

Achieved up to 80% robustness in localization

Successfully estimated local map deformation

Localized robot in correct room despite map inaccuracies

Abstract

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80%