Efficient generic calibration method for general cameras with single centre of projection

Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method

Heuristic method based on voting for extrinsic orientation through image epipolarization

[EN] Traditionally, the stereo-pair rectification, also known as epipolarization problem, (i.e., the projection of both images onto a common image plane) is solved once both intrinsic (interior) and extrinsic (exterior) orientation parameters are known. A heuristic method is proposed to solve both the extrinsic orientation problem and the epipolarization problem in just one single step. The algorithm uses the main property of a coplanar stereopair as fitness criteria: null vertical parallax between corresponding points to achieve the best stereopair. Using an iterative approach, each pair of corresponding points will vote for a rotation axis that may reduce vertical parallax. The votes will be weighted, the rotation applied, and an iteration will be carried out, until the vertical parallax residual error is below a threshold. The algorithm performance and accuracy are checked using both simulated and real case examples. In addition, its results are compared with those obtained using a traditional nonlinear least-squares adjustment based on the coplanarity condition. The heuristic methodology is robust, fast, and yields optimal results.The authors gratefully acknowledge the support from the Spanish Ministerio de Economia y Competitividad to the Project No. HAR2014-59873-R.Martín, S.; Lerma García, JL.; Uzkeda, H. (2017). Heuristic method based on voting for extrinsic orientation through image epipolarization. Journal of Electronic Imaging. 26(6):063020-1-063020-11. https://doi.org/10.1117/1.JEI.26.6.063020S063020-1063020-1126

Real-Time Multi-Fisheye Camera Self-Localization and Egomotion Estimation in Complex Indoor Environments

In this work a real-time capable multi-fisheye camera self-localization and egomotion estimation framework is developed. The thesis covers all aspects ranging from omnidirectional camera calibration to the development of a complete multi-fisheye camera SLAM system based on a generic multi-camera bundle adjustment method

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201