3D Reconstruction with Uncalibrated Cameras Using the Six-Line Conic Variety

We present new algorithms for the recovery of the Euclidean structure from a projective calibration of a set of cameras with square pixels but otherwise arbitrarily varying intrinsic and extrinsic parameters. Our results, based on a novel geometric approach, include a closed-form solution for the case of three cameras and two known vanishing points and an efficient one-dimensional search algorithm for the case of four cameras and one known vanishing point. In addition, an algorithm for a reliable automatic detection of vanishing points on the images is presented. These techniques fit in a 3D reconstruction scheme oriented to urban scenes reconstruction. The satisfactory performance of the techniques is demonstrated with tests on synthetic and real data

Linear Camera Autocalibration with Varying Parameters

We provide a new technique for the Euclidean upgrading of a projective calibration for a set of ten or more cameras with known skew angle and aspect ratio and arbitrary varying focal length and principal point. The proposed algorithm, which is purely linear and thus of very low computational cost and not suffering from initialization problems, is based on the geometric object given by the set of lines incident with the absolute conic. We include experiments which show the good performance of the technique

A feature-based approach for monocular camera tracking in unknown environments

© 2017 IEEE. Camera tracking is an important issue in many computer vision and robotics applications, such as, augmented reality and Simultaneous Localization And Mapping (SLAM). In this paper, a feature-based technique for monocular camera tracking is proposed. The proposed approach is based on tracking a set of sparse features, which are successively tracked in a stream of video frames. In the developed system, camera initially views a chessboard with known cell size for few frames to be enabled to construct initial map of the environment. Thereafter, Camera pose estimation for each new incoming frame is carried out in a framework that is merely working with a set of visible natural landmarks. Estimation of 6-DOF camera pose parameters is performed using a particle filter. Moreover, recovering depth of newly detected landmarks, a linear triangulation method is used. The proposed method is applied on real world videos and positioning error of the camera pose is less than 3 cm in average that indicates effectiveness and accuracy of the proposed method

Monocular Vision SLAM for Indoor Aerial Vehicles

This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping (SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

Biologically Inspired Monocular Vision Based Navigation and Mapping in GPS-Denied Environments

This paper presents an in-depth theoretical study of bio-vision inspired feature extraction and depth perception method integrated with vision-based simultaneous localization and mapping (SLAM). We incorporate the key functions of developed visual cortex in several advanced species, including humans, for depth perception and pattern recognition. Our navigation strategy assumes GPS-denied manmade environment consisting of orthogonal walls, corridors and doors. By exploiting the architectural features of the indoors, we introduce a method for gathering useful landmarks from a monocular camera for SLAM use, with absolute range information without using active ranging sensors. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners. The proposed methods are experimentally validated by our self-contained MAV inside a conventional building