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 .
The use of three-dimensional information to improve the targeting methods for detection of trails was initially explored by @cite_12 @cite_7 . More specifically, @cite_7 used a binocular vision system composed of two omni-directional cameras and a laser scanner to map the vehicle's surrounding area. Thus, this previous method also presents itself as a solid solution for trail segmentation but requires the use of a laser scanner, which should be avoided if size, weight, and energy constraints found in small UAV are to be met. @cite_5 also proposed a system based on binocular vision for detection and tracking of paths in a forest environment. In this paper we offer a solution that is based on monocular vision and, thus, is more easily integrated in current commercial UAV solutions. Moreover, the ability to solve the trail detection problem with a single camera is key for robots equipped with binocular systems when one of the camera fails or when stereo calibration data becomes deprecated.
@cite_5 @cite_7 @cite_12
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2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping. We describe a wheeled robotic system which navigates along outdoor trails intended for hikers and bikers. Through a combination of appearance and structural cues derived from stereo omnidirectional color cameras and a tiltable laser range-finder, the system is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is efficiently segmented in a top-down fashion based on color, brightness, and or height contrast with flanking areas, and a differential motion planner searches for maximally-safe paths within that region according to several criteria. When the trail trackers confidence drops the robot slows down to allow a more detailed search, and when it senses a dangerous situation due to excessive slope, dense trailside obstacles, or visual trail segmentation failure, it stops entirely to acquire and analyze a ladar-derived point cloud in order to reset the tracker. Our systems ability to negotiate a variety of challenging trail types over long distances is demonstrated through a number of live runs through different terrain and in different weather conditions. In this paper, a sliding-window two-camera vision-aided inertial navigation system (VINS) is presented in the square-root inverse domain. The performance of the system is assessed for the cases where feature matches across the two-camera images are processed with or without any stereo constraints (i.e., stereo vs. binocular). To support the comparison results, a theoretical analysis on the information gain when transitioning from binocular to stereo is also presented. Additionally, the advantage of using a two-camera (both stereo and binocular) system over a monocular VINS is assessed. Furthermore, the impact on the achieved accuracy of different image-processing frontends and estimator design choices is quantified. Finally, a thorough evaluation of the algorithm's processing requirements, which runs in real-time on a mobile processor, as well as its achieved accuracy as compared to alternative approaches is provided, for various scenes and motion profiles.