| Summary: | As personal service robots are expected to gain widespread use in the near future there is a need for these robots to function properly in a large number of different environments. In order to acquire such an understanding this thesis focuses on implementing a depth image based planar segmentation method based on the detection of 3-D edges in video-rate speed on an embedded system. The use of plane segmentation as a mean of understanding an unknown environment was chosen after a thorough literature review that indicated that this was the most promising approach capable of reaching video-rate speeds. The camera used to capture depth images is a Kinect for Xbox One, which makes video-rate speed 30 fps, as it is suitable for use in indoor environments and the embedded system is a Jetson TX1 which is capable of running GPU-accelerated algorithms. The results show that the implemented method is capable of segmenting depth images at video-rate speed at half the original resolution. However, full-scale depth images are only segmented at 10-12 fps depending on the environment which is not a satisfactory result.
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