Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

An Experimental Analysis of the Effects of Different Hardware Setups on Stereo Camera Systems

For many application areas such as autonomous navigation, the ability to accurately perceive the environment is essential. For this purpose, a wide variety of well-researched sensor systems are available that can be used to detect obstacles or navigation targets. Stereo cameras have emerged as a very versatile sensing technology in this regard due to their low hardware cost and high fidelity. Consequently, much work has been done to integrate them into mobile robots. However, the existing literature focuses on presenting the concepts and algorithms used to implement the desired robot functions on top of a given camera setup. As a result, the rationale and impact of choosing this camera setup are usually neither discussed nor described. Thus, when designing the stereo camera system for a mobile robot, there is not much general guidance beyond isolated setups that worked for a specific robot. To close the gap, this paper studies the impact of the physical setup of a stereo camera system in indoor environments. To do this, we present the results of an experimental analysis in which we use a given software setup to estimate the distance to an object while systematically changing the camera setup. Thereby, we vary the three main parameters of the physical camera setup, namely the angle and distance between the cameras as well as the field of view and a rather soft parameter, the resolution. Based on the results, we derive several guidelines on how to choose the parameters for an application.

Calibration Technique of a Curved Zoom Compound Eye Imaging System

A calibration method for the designed curved zoom compound eye is studied in order to achieve detection and positioning of spatial objects. The structure of the curved zoom compound eye is introduced. A calibration test platform is designed and built based on the image characteristics of the compound eye, which can be constructed in the large field view for the calibration target. The spot images are obtained through image processing. The center of the spot is calculated by Gauss fitting method. This method is highly simple and intuitive, and it can be used in a zoom surface compound eye without any complex procedures. Finally, the corresponding relationship between the spot center coordinates and the incident light vector of the corresponding sub-eye is established, and the calibration of the multi vision positioning system is completed.

Methods for Simultaneous Robot-World-Hand-Eye Calibration: A Comparative Study

In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called ‘hand–eye’ and ‘robot-world-hand–eye’, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.