Monocular vision avoidance method based on fully convolutional networks

Visual obstacle avoidance is a practical application of machine vision technology. With the development of unmanned and artificial intelligence, visual obstacle avoidance technology has become a research hotspot, because the avoiding obstacle is an indispensable ability for robots to explore the unknown world. The traditional methods often rely on edge detection or feature point extraction, which has poor robustness and is difficult to meet practical applications. Convolutional neural networks (CNNs) shine in a variety of machine vision problems (image classification, target detection, image segmentation, image generation, etc.), showing an obviously robustness over traditional algorithms. Based on this, this paper proposes a method to solve the task of avoiding obstacle by using the Fully convolutional networks (FCNs) to extract accessible area. This paper also proves the robustness and effectiveness of the method through a series of experiments.