The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved.

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors / M. Luperto, F. Amadelli, M. Di Berardino, F. Amigoni. - In: ROBOTICS AND AUTONOMOUS SYSTEMS. - ISSN 0921-8890. - 159:(2023), pp. 104282.1-104282.15. [10.1016/j.robot.2022.104282]

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

M. Luperto
Primo
;
2023

Abstract

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved.
Mapping; Map prediction; Doors
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
24-set-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/950944
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