Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges. One main reason is that GPS has limited precision in indoor environments. The additional fact that MAVs are not able to carry heavy weight or power consuming sensors, such as range finders, makes indoor autonomous navigation a challenging task. In this paper, we propose a practical system in which a quadcopter autonomously navigates indoors and finds a specific target, i.e., a book bag, by using a single camera. A deep learning model, Convolutional Neural Network (ConvNet), is used to learn a controller strategy that mimics an expert pilot's choice of action. We show our system's performance through real-time experiments in diverse indoor locations. To understand more about our trained network, we use several visualization techniques.
: Accurate depth estimation and relative position estimation are possible using stereo cameras ( @cite_12 , @cite_11 ). However, stereo vision algorithms suffer when they are used in texture-less regions, as it is hard to match features in one image to the corresponding features in the other image. The additional fact that most of publicly available quadcopters have only one built-in camera makes the solution not practical to a public. Our system shows robust performance in texture-less environments.
@cite_12 @cite_11
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Depth estimation in computer vision and robotics is most commonly done via stereo vision (stereopsis), in which images from two cameras are used to triangulate and estimate distances. However, there are also numerous monocular visual cues--such as texture variations and gradients, defocus, color haze, etc. --that have heretofore been little exploited in such systems. Some of these cues apply even in regions without texture, where stereo would work poorly. In this paper, we apply a Markov Random Field (MRF) learning algorithm to capture some of these monocular cues, and incorporate them into a stereo system. We show that by adding monocular cues to stereo (triangulation) ones, we obtain significantly more accurate depth estimates than is possible using either monocular or stereo cues alone. This holds true for a large variety of environments, including both indoor environments and unstructured outdoor environments containing trees forests, buildings, etc. Our approach is general, and applies to incorporating monocular cues together with any off-the-shelf stereo system. A new method for actively recovering depth information using image defocus is demonstrated and shown to support active stereo vision depth recovery by providing monocular depth estimates to guide the positioning of cameras for stereo processing. This active depth-from-defocus approach employs a spatial frequency model for image defocus which incorporates the optical transfer function of the image acquisition system and a maximum likelihood estimator to determine the amount of defocus present in a sequence of two or more images taken from the same pose. This defocus estimate is translated into a measurement of depth and associated uncertainty that is used to control the positioning of a variable baseline stereo camera system. This cooperative arrangement significantly reduces the matching uncertainty of the stereo correspondence process and increases the depth resolution obtainable with an active stereo vision platform.