Depth camera SLAM on a low-cost WiFi mapping robot
Technologies for Practical Robot Applications(2012)
摘要
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.
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关键词
slam (robots),cameras,collision avoidance,mobile robots,particle filtering (numerical methods),robot vision,wireless lan,6-degrees-of-freedom rgb-d slam,kinect depth camera,rgb-d images,wifi localization,wifi mapping robot,depth camera slam,indoor localization,loop closures,mobile robotic platform,particle filtering 2d slam algorithms,radio-frequency fingerprinting,real-time obstacle-avoidance-based navigation,simultaneous localization and mapping,two-stage localization architecture,visual-based odometry correction,depth camera,mapping,navigation,field of view,real time,mobile robot,degree of freedom,obstacle avoidance,particle filter,radio frequency,signal strength,robot kinematics
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