Deep learning navigation for UGVs on forests paths

Abstract: Artificial intelligence and machine learning have seen great progress in recent years. In this work, we will look at the application of machine learning in visual navigational systems for unmanned vehicles in natural environments. Previous works have focused on navigational systems with deep convolutional neural networks (CNNs) for unmanned aerial vehicles (UAVs). In this work, we evaluate the robustness and applicability of these methods for unmanned ground vehicles (UGVs). To evaluate the robustness and applicability of this machine learning approach for UGV two experiments where performed. In the first, data from Swiss trails and photos collected in Swedish forests where used to train deep CNNs. Several models are trained using data collected in different environments at different heights. By cross evaluating the trained models on the other datasets the impact of changing camera position and switching environment can be evaluated. In the second experiment, a navigational system using the trained CNN models were constructed. By evaluating the ability of the system to autonomously follow a forest path an understanding of the applicability of these methods for UGVs in general can be obtained. There where several results from the experiments. When comparing models trained on different datasets, we could see that the environment has an effect on the performance of the navigation, but even more so, the approach is sensitive to the camera position. Finally, an online test to evaluate the applicability of this approach as an end-to-end navigation system for UGVs is done. This experiment showed that these methods, on their own, are not a viable option for an end-to-end navigational system for UGVs in forest environments.