text: 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.
  @cite_5: ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.
  @cite_7: 
  @cite_6: We consider the problem of autonomously flying Miniature Aerial Vehicles (MAVs) in indoor environments such as home and office buildings. The primary long range sensor in these MAVs is a miniature camera. While previous approaches first try to build a 3D model in order to do planning and control, our method neither attempts to build nor requires a 3D model. Instead, our method first classifies the type of indoor environment the MAV is in, and then uses vision algorithms based on perspective cues to estimate the desired direction to fly. We test our method on two MAV platforms: a co-axial miniature helicopter and a toy quadrotor. Our experiments show that our vision algorithms are quite reliable, and they enable our MAVs to fly in a variety of corridors and staircases.
summary: : Other approach uses vanishing points. @cite_6 used a monocular camera and found vanishing points. The points were used to fly in corridor environments. For staircase environments, they found center of the staircase. A front-facing short-range sensor, however, was additionally used to avoid collisions in corners and unknown environments. Our approach does not require the additional range sensor and can successfully perform collision avoidance with a monocular camera. Another approaches that are most closely related to our approach are approaches that learn control policies from input data. The ALVINN project @cite_5 showed how the 3-layer artificial neural networks imitated a human driver’s response on road and performed autonomous vehicle driving. @cite_7 applied a novel imitation learning strategy, the DAgger Algorithm, and learned a controller policy that imitated human pilot’s choice of action from demonstrations of the desired behavior. The system demonstrated a fast autonomous flight in natural forest environments. We extend these learning approaches and employ an advanced classifier, ConvNets, which learns to autonomously fly and finds a target based on purely visual input.