Monte Carlo Localization in Hand-Drawn Maps
Bahram Behzadian, Pratik Agarwal, Wolfram Burgard, Gian Diego, Tipaldi

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%
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