This leads to a combination of novel detection, description, and matching steps. This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor) and by simplifying these methods to the essential. SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). We believe that the proposed system can potentially change the consumer habits and becomes the new type of home-use handheld camcorder system in the future. The proposed system has eight cameras with maximum viewing angle of 90° in divergence mode, 38 mm spacing in parallel mode, and imaging radius of 10 m ∼ 0.5 m in convergence mode. The proposed system can then insert virtual viewpoint images between actual viewpoint images to allow the viewpoint switching more smoothly. The second stage requires no auxiliary tool but utilizes a large number of common feature points from multiple viewpoint images to acquire the extrinsic parameters (translation and rotation matrix) and to compensate the vertical misalignment and the horizontal uneven angle distribution due to the mechanical structure. The first stage adopts the traditional checkerboard calibration method to get the intrinsic parameters (focal length, principal point) and the lens distortion for each camera. To rapidly acquire the relationship among cameras after configuration change, we propose a two-stage calibration method to compensate the mechanical misalignment. The three camera configurations can each be suitable for applications such as panorama image stitching, autostereoscopic 3D display, bullet-time (time-freeze) visual effect, 3D scene reconstruction, etc. With the proposed system, the users can push one single button to change the configuration of the camera array rapidly to divergence (convex arc), parallel (linear), or convergence (concave arc). The proposed system differs from the traditional fixed image acquisition systems which are large-sized, high-priced, single functional, and can only captured images at specific locations. The CrossbowCam is suitable for multi-viewpoint image acquisition, smooth switching, alignment and seamless stitching applications. This paper presents a novel multi-functional, low-cost handheld multi-camera system (one dimensional camera array) - “CrossbowCam”. During GPS outage for 83 s, the proposed method based on the GPS/RAVO/MEMS-INS smartphone integrated CKF method could provide a reliable USV navigation solution it reduced the position error to 82.32% compared to the traditional GPS/MEMS-INS CKF method, and to about 70.72% compared to the GPS/Pure-VO/MEMS-INS CKF integrated method. The efficiency of the USV navigation system is tested on a surface reference trajectory called Kur-Mukalla in Mukalla City, Yemen. The CKF is used as data fusion processing to estimate and correct USV navigation system errors. The DVL/depth/compass/MEMS-INS smartphone integrated solution is used to improve the performance of a VO system before data fusion processing using a CKF. It is based on integrated visual odometry (VO) with MEMS-INS smartphone sensors, the Doppler velocity log (DVL), depth and compass sensors. The proposed RAVO is used to correct USV navigation system errors during GPS outages. This paper provides a continuous and accurate navigation solution via integrated GPS with micro-electro-mechanical (MEMS)-INS smartphone sensors and reduced-aided visual odometry (RAVO) using centralized Kalman filter (CKF) data fusion. But in some places on the Earth's surface, the GPS signal suffers from outages, interferences, and weakness, which affect the performance of the whole USV navigation system. Most navigation systems of unmanned surface vehicles (USVs) are based on global position system (GPS)/inertial navigation system (INS) integrated methods to improve the accuracy of the navigation system.

Eigenmath 1.37