Depth camera SLAM on a low-cost WiFi mapping robot

• Published in: 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA) pp. 1 - 6
• Main Authors: Mirowski, P., Palaniappan, R., Tin Kam Ho
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2012
• Subjects: Buildings; Cameras; depth camera; indoor localization; mapping; Mobile robots; navigation; Robot kinematics; Robot sensing systems
• ISBN: 9781467308557; 1467308552
• ISSN: 2325-0526
• DOI: 10.1109/TePRA.2012.6215673

Radio-Frequency fingerprinting is an interesting solution for indoor localization. It exploits existing telecommunication infrastructure, such as WiFi routers, along with a database of signal strengths at different locations, but requires manually collecting signal measurements along with precise position information. To automatically build signal maps, we use an autonomous, self-localizing, low-cost mobile robotic platform. Our robot relies on the Kinect depth camera that is limited by a narrow field of view and short range. Our two-stage localization architecture first performs real-time obstacle-avoidance-based navigation and visual-based odometry correction for bearing angles. It then uses RGB-D images for Simultaneous Localization and Mapping. We compare the applicability of 6-degrees-of-freedom RGB-D SLAM, and of particle filtering 2D SLAM algorithms and present novel ideas for loop closures. Finally, we demonstrate the use of the robot for WiFi localization in an office space.