text: This article addresses the navigation problem of small and medium-sized unmanned aerial vehicles (UAVs) built to perform missions in forest environments (e.g., search and rescue) by exploiting the presence of natural or man-made paths, typically present in this type of environments. The proposed system extends a previous monocular-based technique for trail detection and tracking so as to take into account volumetric data acquired from a Visual SLAM algorithm and, as a result, to increase its sturdiness upon challenging trails. The experimental results, obtained via a set of 12 videos recorded with a camera installed in a tele-operated, unmanned small-sized aerial vehicle, show the ability of the proposed system to overcome some of the difficulties of the original detector, attaining a success rate of 97.8 .
  @cite_24: 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.
  @cite_21: This paper presents results demonstrating real-time six degree of freedom localization, mapping, navigation and obstacle avoidance in an outdoor environment using only a lowcost o-the-shelf inertial measurement unit and a monocular camera. This navigation system is intended for operation of small unmanned aerial vehicles when GPS signals are unavailable due to obstructions or jamming and when operating in cluttered environments such as urban canyons or forests. A small radio-controlled car is used as a test bed. A bearings-only Simultaneous Localization and Mapping algorithm was implemented to localize both the vehicle and obstacles. This paper describes: (a) the hardware used; (b) a two-step approach for data association (image frame to image frame followed by image frame to map); (c) a technique for landmark initialization for the case where landmarks are located on the ground. Hardware test results demonstrating navigation to a goal in an obstacle strewn environment are presented. The eect of unmodelled sensor biases is examined in simulation.
  @cite_13: 
  @cite_17: This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.
summary: The models referred in the previous paragraph are the basis of most work on path detection. An example is the use of knowledge about the color distributions of both the path and their surroundings for segmentation @cite_21 . The main direction of the path can also be learned off-line in a supervised way @cite_24 . The use of off-line learned models has the limitation that the robot is only able to perceive environments that have been covered by the training set. Robustness can be increased if these models are substituted by models learned on-line @cite_13 @cite_17 . In contrast to the road domain, the definition of the reference regions from which it is possible to supervise the learning process is challenging. With varying width and orientation, it is difficult to assure that the robot is on the path, and from that, which regions of the input image can be used as reference. Moreover, paths and its surroundings often exhibit the same height, which hampers a straightforward use of depth information to determine a path reference patch.