Depth camera SLAM on a low-cost WiFi mapping robot

Piotr Mirowski, Ravishankar Palaniappan, Tin Kam Ho

IEEE International Conference on Technologies for Practical Robot Applications (TePRA), 2012

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-offreedom 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.

Paper link: Research Gate.