期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2015
卷号：12
期号：11
页码：156
DOI：10.5772/61348
语种：English
出版社：SAGE Publications
摘要：The aim of this paper is to deploy a time-of-flight (ToF) based photonic mixer device (PMD) camera on an Autonomous Ground Vehicle (AGV) whose overall target is to traverse from one point to another in hazardous and hostile environments employing obstacle avoidance without human intervention. The hypothesized approach of applying a ToF Camera for an AGV is a suitable approach to autonomous robotics because, as the ToF camera can provide three-dimensional (3D) information at a low computational cost, it is utilized to extract information about obstacles after their calibration and ground testing and is mounted and integrated with the Pioneer mobile robot. The workspace is a two-dimensional (2D) world map which has been divided into a grid/cells, where the collision-free path defined by the graph search algorithm is a sequence of cells the AGV can traverse to reach the target. PMD depth data is used to populate traversable areas and obstacles by representing a grid/cells of suitable size. These camera data are converted into Cartesian coordinates for entry into a workspace grid map. A more optimal camera mounting angle is needed and adopted by analysing the camera's performance discrepancy, such as pixel detection, the detection rate and the maximum perceived distances, and infrared (IR) scattering with respect to the ground surface. This mounting angle is recommended to be half the vertical field-of-view (FoV) of the PMD camera. A series of still and moving tests are conducted on the AGV to verify correct sensor operations, which show that the postulated application of the ToF camera in the AGV is not straightforward. Later, to stabilize the moving PMD camera and to detect obstacles, a tracking feature detection algorithm and the scene flow technique are implemented to perform a real-time experiment.
关键词：3D ToF Camera; PMD Camera; Pioneer Mobile Robots; Path-planning
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