Navigability Assessment for Autonomous Systems Using Deep Neural Networks
Abstract: Automated navigability assessment based on image sensor data is an important concern in the design of autonomous robotic systems. The problem consists in finding a mapping from input data to the navigability status of different areas of the surrounding world. Machine learning techniques are often applied to this problem. This thesis investigates an approach to navigability assessment in the image plane, based on offline learning using deep convolutional neural networks, applied to RGB and depth data collected using a robotic platform. Training outputs were generated by manually marking out instances of near collision in the sequences and tracing back the location of the near-collision frame through the previous frames. Several combinations of network inputs were tried out. Inputs included grayscale gradient versions of the RGB frames, depth maps, image coordinate maps and motion information in the form of a previous RGB frame or heading maps. Some improvement compared to simple depth thresholding was demonstrated, mainly in the handling of noise and missing pixels in the depth maps. The resulting networks appear to be mostly dependent on depth information; an attempt to train a network without the depth frames was unsuccessful,and a network trained using the depth frames alone performed similarly to networks trained with additional inputs. An unsuccessful attempt at training a network towards a more motion-dependent navigability concept was also made. It was done by including training frames captured as the robot was moving away from the obstacle, where the corresponding training outputs were marked as obstacle-free.
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