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Scene understanding for autonomous vehicles is a challenging computer visiontask, with recent advances in convolutional neural networks (CNNs) achievingresults that notably surpass prior traditional feature driven approaches.However, limited work investigates the application of such methods eitherwithin the highly unstructured off-road environment or to RGBD input data. Inthis work, we take an existing CNN architecture designed to perform semanticsegmentation of RGB images of urban road scenes, then adapt and retrain it toperform the same task with multichannel RGBD images obtained under a range ofchallenging off-road conditions. We compare two different stereo matchingalgorithms and five different methods of encoding depth information, includingdisparity, local normal orientation and HHA (horizontal disparity, height aboveground plane, angle with gravity), to create a total of ten experimentalvariations of our dataset, each of which is used to train and test a CNN sothat classification performance can be evaluated against a CNN trained usingstandard RGB input.

Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network