Monte Carlo Localization in Hand-Drawn Maps
This addresses a practical problem for robotics in unstructured environments where pre-existing maps are unavailable, though it appears incremental as it adapts existing methods to a new scenario.
The paper tackles robot localization using hand-drawn maps from non-experts, achieving up to 80% robustness in room-level localization by estimating local map deformations.
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%