Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.
More similar to our research, implemented an efficient optic flow algorithm on a small lightweight (2 g) omnidirectional camera system on a 30 g helicopter @cite_25 . With a ring of 8 low-resolution image chips (64 x 64 pixels), the MAV could compute optical flow. It did this by computing the edges, compressing the images and calculate the displacement by block matching which resulted in translational optical flow. The vision calculations where done on-board the helicopter with 10 Hz, yet the flight controls where computed off-board. Although the potential of a full on-board implementation is there, the redundancy lies in the ratio of cameras to sensed variables. One camera has the potential of detecting flow in 3 directions; they used 8 to only detect 2 (forward and sideways velocity).
@cite_25
2087387266 1484557735 1975326224 2088692258
In this paper, we describe our autonomous vision-based quadrotor MAV system which maps and explores unknown environments. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main exteroceptive sensor, our quadrotor achieves these capabilities with both the Vector Field Histogram+ (VFH+) algorithm for local navigation, and the frontier-based exploration algorithm. In addition, we implement the Bug algorithm for autonomous wall-following which could optionally be selected as the substitute exploration algorithm in sparse environments where the frontier-based exploration under-performs. We incrementally build a 3D global occupancy map on-board the MAV. The map is used by the VFH+ and frontier-based exploration in dense environments, and the Bug algorithm for wall-following in sparse environments. During the exploration phase, images from the front-looking camera are transmitted over Wi-Fi to the ground station. These images are input to a large-scale visual SLAM process running off-board on the ground station. SLAM is carried out with pose-graph optimization and loop closure detection using a vocabulary tree. We improve the robustness of the pose estimation by fusing optical flow and visual odometry. Optical flow data is provided by a customized downward-looking camera integrated with a microcontroller while visual odometry measurements are derived from the front-looking stereo camera. We verify our approaches with experimental results. In this paper, we propose a novel and computationally efficient algorithm for simultaneous exploration and coverage with a vision-guided micro aerial vehicle (MAV) in unknown environments. This algorithm continually plans a path that allows the MAV to fulfil two objectives at the same time while avoiding obstacles: observe as much unexplored space as possible, and observe as much of the surface of the environment as possible given viewing angle and distance constraints. The former and latter objectives are known as the exploration and coverage problems respectively. Our algorithm is particularly useful for automated 3D reconstruction at the street level and in indoor environments where obstacles are omnipresent. By solving the exploration problem, we maximize the size of the reconstructed model. By solving the coverage problem, we maximize the completeness of the model. Our algorithm leverages the state lattice concept such that the planned path adheres to specified motion constraints. Furthermore, our algorithm is computationally efficient and able to run on-board the MAV in real-time. We assume that the MAV is equipped with a forward-looking depth-sensing camera in the form of either a stereo camera or RGB-D camera. We use simulation experiments to validate our algorithm. In addition, we show that our algorithm achieves a significantly higher level of coverage as compared to an exploration-only approach while still allowing the MAV to fully explore the environment. The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow. This paper presents a method for scene flow estimation from a calibrated stereo image sequence. The scene flow contains the 3-D displacement field of scene points, so that the 2-D optical flow can be seen as a projection of the scene flow onto the images. We propose to recover the scene flow by coupling the optical flow estimation in both cameras with dense stereo matching between the images, thus reducing the number of unknowns per image point. The use of a variational framework allows us to properly handle discontinuities in the observed surfaces and in the 3-D displacement field. Moreover our approach handles occlusions both for the optical flow and the stereo. We obtain a partial differential equations system coupling both the optical flow and the stereo, which is numerically solved using an original multi- resolution algorithm. Whereas previous variational methods were estimating the 3-D reconstruction at time t and the scene flow separately, our method jointly estimates both in a single optimization. We present numerical results on synthetic data with ground truth information, and we also compare the accuracy of the scene flow projected in one camera with a state-of-the-art single-camera optical flow computation method. Results are also presented on a real stereo sequence with large motion and stereo discontinuities. Source code and sample data are available for the evaluation of the algorithm.