This paper presents the optimization of a camera placement for improved localization accuracy. It is the basis of a localization system aiming for high localization accuracy. This goal is achieved using several cameras with very redundant fieldof-views. The paper presents the calculation of the localization accuracy -which depends on the camera model and the pixel quantizing error -at one specific point. A method is introduced for handling areas in a probabilistic sense instead of examining only one point. The accuracy can be improved by adding a new camera to the system. The calculation of the optimal position of the new camera at compliance to some restrictions is demonstrated. The Smart Mobile Eyes for Localization (SMEyeL) project is open-source: the source code, all measurement input data and documentation are public available.

This paper presents a marker localization system consisting of multiple cameras. It is the basis of a real-time robotic motion tracking system aiming for high localization accuracy and high time resolution. These goals are achieved using several cameras with very redundant field-of-views. The paper presents the theoretical background of 3D camera calibration and localization, the localization accuracy measurement setup, and its results. The measurements are obtained using a color marker and an industrial robotic arm for measurement ground truth generation. The Smart Mobile Eyes for Localization (SMEyeL) project is open-source: the source code, all measurement input data and documentation are public available.

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