Informed Data Selection For Dynamic Multi-Camera Clusters

Traditional multi-camera systems require a fixed calibration between cameras to provide the solution at the correct scale, which places many limitations on its performance. This thesis investigates the calibration of dynamic camera clusters, or DCCs, where one or more of the cluster cameras is mounted to an actuated mechanism, such as a gimbal or robotic manipulator. Our novel calibration approach parameterizes the actuated mechanism using the Denavit-Hartenberg convention, then determines the calibration parameters which allow for the estimation of the time varying extrinsic transformations between the static and dynamic camera frames. A degeneracy analysis is also presented, which identifies redundant parameters of the DCC calibration system. In order to automate the calibration process, this thesis also presents two information theoretic methods which selects the optimal calibration viewpoints using a next-best-view strategy. The first strategy looks at minimizing the entropy of the calibration parameters, while the second method selects the viewpoints which maximize the mutual information between the joint angle input and calibration parameters. Finally, the effective selection of key-frames is also an essential aspect of robust visual navigation algorithms, as it ensures metrically consistent mapping solutions while reducing the computational complexity of the bundle adjustment process. To that end, we propose two entropy based methods which aim to insert key-frames that will directly improve the system's ability to localize. The first approach inserts key-frames based on the cumulative point entropy reduction in the existing map, while the second approach uses the predicted point flow discrepancy to select key-frames which best initialize new features for the camera to track against in the future. The DCC calibration methods are verified in both simulation and using physical hardware consisting of a 5-DOF Fanuc manipulator and a 3-DOF Aeryon Skyranger gimbal. We demonstrate that the proposed methods are able to achieve high quality calibrations using RMSE pixel error metrics, as well as through analysis of the estimator covariance matrix. The key-frame insertion methods are implemented within the Multi-Camera Parallel Mapping and Tracking (MCPTAM) framework, and we confirm the effectiveness of these approaches using high quality ground truth collected using an indoor positioning system

Relative Pose Estimation Using Non-overlapping Multicamera Clusters

This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments

Authors’s version. The final publication is available at www.springerlink.com. Degeneracy of the Linear Seventeen-Point Algorithm for Generalized Essential Matrix

Abstract In estimating motions of multi-centered optical systems using the generalized camera model, one can use the linear seventeen-point algorithm for obtaining a generalized essential matrix, the counterpart of the eight-point algorithm for the essential matrix of a pair of cameras. Like the eight-point algorithm, the seventeen-point algorithm has degenerate cases. However, mechanisms of the degeneracy of this algorithm have not been investigated. We propose a method to find degenerate cases of the algorithm by decomposing a measurement matrix that is used in the algorithm into two matrices about ray directions and centers of projections. This decomposition method allows us not only to prove degeneracy of the previously known degenerate cases, but also to find a new degenerate configuration