A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Abstract

We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a Deep Neural Network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first paper that describes an approach to perceive forest trials which is demonstrated on a quadrotor micro aerial vehicle.

Safe Visual Navigation via Deep Learning and Novelty Detection

“…Barnes et al [3] and Oliveira et al [24] provide more recent examples of deep learning used to segment image pixels into drivable routes, which can then be used for planning. While we focus on supervised learning of collision probability, there are many other ways to map between images and actions, including end-to-end neural networks [6,19], affordance-based representations [7], reinforcement learning using either simulated or real images [23,31], and classification of paths from manually collected data [11]. While it would be impossible to survey the vast literature on visual navigation here, we note that these examples and many others do not explicitly address uncertainty about the learned model.…”

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“…More recently, Giusti et al (Giusti et al, 2016) showed that a monocular system can be trained to follow a hiking path. But once again, only 2D movement is approached, asking a UAV going forward to change its yaw based on likeliness to be following a traced path.…”

A Convolutional Neural Networks Approach to Devise Controller

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