No abstract

This paper presents an approach for online estimation of the extrinsic calibration parameters of a multicamera rig. Given a coarse initial estimate of the parameters, the relative poses between cameras are refined through recursive filtering. The approach is purely vision based and relies on plane induced homographies between successive frames. Overlapping fields of view are not required. Instead, the ground plane serves as a natural reference object. In contrast to other approaches, motion, relative camera poses, and the ground plane are estimated simultaneously using a single iterated extended Kalman filter. This reduces not only the number of parameters but also the computational complexity. Furthermore, an arbitrary number of cameras can be incorporated. Several experiments on synthetic as well as real data were conducted using a setup of four synchronized wide angle fisheye cameras, mounted on a moving platform. Results were obtained, using both, a planar and a general motion model with full six degrees of freedom. Additionally, the effects of uncertain intrinsic parameters and nonplanar ground were evaluated experimentally.

Camera calibration is a prerequisite for many computer vision applications. While a good calibration can turn a camera into a measurement device, it can also deteriorate a system’s performance if not done correctly. In the recent past, there have been great efforts to simplify the calibration process. Yet, inspection and evaluation of calibration results typically still requires expert knowledge. In this work, we introduce two novel methods to capture the fundamental error sources in camera calibration: systematic errors (biases) and remaining uncertainty (variance). Importantly, the proposed methods do not require capturing additional images and are independent of the camera model. We evaluate the methods on simulated and real data and demonstrate how a state-of-the-art system for guided calibration can be improved. In combination, the methods allow novice users to perform camera calibration and verify both the accuracy and precision. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-71278-5_3) contains supplementary material, which is available to authorized users.

No abstract

Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system's overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras.