Robust Localization with Architectural Floor Plans and Depth Camera
Paper in proceedings, 2020

Autonomous navigation is of great importance for service robots. Such robots need various technologies, especially localization, and mapping. In this paper, we focus on the localization problem. Commonly, to solve the localization problem, the creation of a map is needed. However, creating the map takes considerable time and costs. Therefore, one solution for indoor environment is to use architectural floor plans since buildings have own floor plans. If a robot can use them for localization, it enables users to cut the time and costs for creating the map from scratch. However, the floor plans sometimes do not match with a real building. Besides, sensor measurement sometimes contains objects such as a pedestrian, which are not contained in the floor plans. In this paper, we propose a localization algorithm with architectural floor plans that is robust to such inconsistencies. The algorithm estimates a robot coordinate by matching the floor plan with the point clouds obtained from depth images. Outliers derived from the inconsistencies in the point clouds are filtered with plane information from the depth images with the Generalized ICP framework. We tested our algorithm with floor plans in a real building and in a simulator as a case study. The results show that our algorithm can localize a robot with average more than twice accuracy compared to AMCL and be used for real-time applications. © 2020 IEEE.

Author

Yoshiaki Watanabe

Takenaka Corporation

Karinne Ramirez-Amaro

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Bahriye Ilhan

Istanbul Technical University (ITÜ)

Taku Kinoshita

Hokkaido University

Technical University of Munich

Thomas Bock

Technical University of Munich

Gordon Cheng

Technical University of Munich

Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

133-138 9025984

2020 IEEE/SICE International Symposium on System Integration, SII 2020
Honolulu, USA,

Subject Categories

Robotics

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/SII46433.2020.9025984

More information

Latest update

10/23/2020