We present an approach for sensor network localization when provided with a set of angular constraints. This problem arises in camera networks when angles between nearby points can be measured but depth measurements are not readily available. We provide contributions for two different variations on this problem. First, when each node is aware of a global coordinate frame, we present a novel method for identifying the components of the problem that are rigidly constrained. Second, in the more difficult case where only relative angles are known, we propose a novel spectral solution that achieves a globally-optimal embedding under transitively-triangular constraints, which we show encompass a wide range of real-world conditions. We demonstrate the utility of our algorithm on both synthetic data and data from quadrotor robot formations.

This paper presents two new, efficient solutions to the two-view, relative pose problem from three image point correspondences and one common reference direction. This three-plus-one problem can be used either as a substitute for the classic five-point algorithm, using a vanishing point for the reference direction, or to make use of an inertial measurement unit commonly available on robots and mobile devices where the gravity vector becomes the reference direction. We provide a simple, closed-form solution and a solution based on algebraic geometry which offers numerical advantages. In addition, we introduce a new method for computing visual odometry with RANSAC and four point correspondences per hypothesis. In a set of real experiments, we demonstrate the power of our approach by comparing it to the five-point method in a hypothesize-and-test visual odometry setting.

In recent years polynomial solvers based on algebraic geometry techniques, and specifically the action matrix method, have become popular for solving minimal problems in computer vision. In this paper we develop a new method for reducing the computational time and improving numerical stability of algorithms using this method. To achieve this, we propose and prove a set of algebraic conditions which allow us to reduce the size of the elimination template (polynomial coefficient matrix), which leads to faster LU or QR decomposition. Our technique is generic and has potential to improve performance of many solvers that use the action matrix method. We demonstrate the approach on specific examples, including an image stitching algorithm where computation time is halved and single precision arithmetic can be used.

We propose a new method for extrinsic calibration of a line-scan LIDAR with a perspective projection camera. Our method is a closed-form, minimal solution to the problem. The solution is a symbolic template found via variable elimination and the multi-polynomial Macaulay resultant. It does not require initialization, and can be used in an automatic calibration setting when paired with RANSAC and least-squares refinement. We show the efficacy of our approach through a set of simulations and a real calibration.

We present an innovative path following system based upon multi-camera visual odometry and visual landmark matching. This technology enables reliable mobile robot navigation in real world scenarios including GPS-denied environments both indoors and outdoors. We recover paths in full 3D, making it applicable to both on and off-road ground vehicles. Our controller relies on pose updates from visual odometry, allowing us to achieve path following even when only a joystick drive interface to the base robot platform is available. We experimentally investigate two specific applications of our technology to autonomous navigation on ground vehicles - non line-of-sight leader-following (between heterogeneous platforms) and retro-traverse to home base. For safety and reliability we add dynamic short range obstacle detection and reactive avoidance capabilities to our controller. We show the results for end-to-end real time implementation of this technology using current off-the-shelf computing and network resources in challenging environments.

We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of vision algorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data and a distributed visual landmark matching capability with geometric consistency verification. We apply these techniques to implement a tag-along robot, where a human operator leads the way and a robot autonomously follows. We show results for a real-time implementation of such a system with real field constraints on CPU power and network resources.

Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal