Camera based Navigation: AI vs Image Processing

Abstract

We compare the performance of rule-based image processing algorithms for autonomous navigation with that of a neural network approach based on an Nvidia CNN architecture. We detail our implementation of Hough-Transform-based and pixel-based navigation algorithms and discuss their advantages and disadvantages, including a quantitative comparison of their navigation performance in terms of speed, stability and reliability. We describe how rule-based algorithms can be used to train neural networks that result in enhanced navigation with high stability and reliability. We discuss the benefits of training neural networks with binary images rather than color images. We present our experiments on autonomous navigation with model cars racing on unknown tracks using the different algorithms and conclude with some recommendations for the use of each algorithm.