WayIL: Image-based Indoor Localization with Wayfinding Maps

Obin Kwon, Dongki Jung, Youngji Kim, Soohyun Ryu, Suyong Yeon, Songhwai Oh, Donghwan Lee

Abstract: This paper tackles a localization problem in large-scale indoor environments with wayfinding maps. A wayfinding map abstractly portrays the environment, and humans can localize themselves based on the map. However, when it comes to using it for robot localization, large geometrical discrepancies between the wayfinding map and the real world make it hard to use conventional localization methods. Our objective is to estimate a robot pose within a wayfinding map, utilizing RGB images from perspective cameras. We introduce two different imagination modules which are inspired by how humans can comprehend and interpret their surroundings for localization purposes. These modules jointly learn how to effectively observe the first-person-view (FPV) world to interpret bird-eye-view (BEV) maps. Providing explicit guidance to the two imagination modules significantly improves the precision of the localization system. We demonstrate the effectiveness of the proposed approach using real-world datasets, which are collected from various large-scale crowded indoor environments. The experimental results show that, in 85% of scenarios, the proposed localization system can estimate its pose within 3m in large indoor spaces. Project Site: https://rllab-snu.github.io/projects/WayIL/