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Time-of-flight camera based Probabilistic Polygonal Mesh mapping

In the past decade, time-of-flight cameras – optical cameras that measure the depth of each pixel – have been made available. Combined with a traditional color camera, this type of sensor provides Red-Green-BlueDepth (RGBD) images at high frequencies. With the mass-produced Microsoft Kinect introduced in late 2010, 30Hz 640 ∗ 480 RGB images are available for less than $200. This source of information, while not exempt of problems, is what the stereo vision community has been striving to obtain for years. We believe this new wealth of information, combined with state-of-the-art Simultaneous Localization And Mapping (SLAM) techniques such as visual SLAM [1], sets the stage for a practical revolution in robotics and artifical intelligence. Indeed, we now have at our disposal methods for sensing geometric, visual and auditory information, all spatialized in the same global reference frame and bound together by SLAM. Because those modalities are the dominant forms of information for humans, robots now have all the essential tools for moving in and interacting with the physical world (geometry), recognizing common objects (vision), and communicating with humans (audition). As a sensor itself, time-of-flight technology has several advantages and disadvantages. At around 10 frames per second, it is possible to collect 3D data of a scene much faster than a pan-tilt laser scan. This increase in speed is significant especially for scene reconstruction for mobile robots, which cannot wait the 5-10s required to obtain one 3D scan from a laser. The major drawback of TOF technology is its high frame-to-frame noise, especially off reflective surfaces. It is this unreliability that we attack in this paper, to improve the TOF sensor’s utility for robotics, vision, and other fields. In the following sections, we describe a new method to transform TOF data into a probabilistically smoothed polygonal mesh. We first explain how the robot could internally represent its surroundings as a mesh representation. Then we describe a method for maintaining this mesh in an online manner, through triangulation of point estimates and converging to stable values through maximum likelihood estimation.