Denis Grießbach

Geometrical camera calibration with diffractive optical elements

Traditional methods for geometrical camera calibration are based on calibration grids or single pixel illumination by collimated light. A new method for geometrical sensor calibration by means of diffractive optical elements (DOE) in connection with a laser beam equipment is presented. This method can be especially used for 2D-sensor array systems but in principle also for line scanners.

Stereo-vision-aided inertial navigation for unknown indoor and outdoor environments

Reliable knowledge of position and orientation is a prerequisite for many applications regarding unmanned navigation, mapping, or environmental modeling. GNSS-aided inertial navigation is the preferred solution for outdoor applications with the well-known weaknesses and limitations. In order to overcome these, alternative technologies have to be investigated and developed. Therefore a stereo-vision-aided inertial navigation system is presented which is capable of providing real-time local navigation. It neither needs a priori knowledge of the environment nor external references. A method is described to reconstruct the egomotion of a stereo camera system aided by inertial data. In turn, the extracted egomotion is used to constrain the inertial sensor drift. The optical information is derived from natural landmarks, extracted, and tracked over consecutive stereo image pairs. Using inertial data for feature tracking effectively reduces computational costs and at the same time increases the reliability. Additionally, the measured gravity serves as a vertical reference, stabilizing the navigation solution. An Integrated Positioning System (IPS) was developed and tested on real data. Vision data and inertial measurements are fused within an error state space Kalman filter. To do so, aspects like precise and reliable synchronization, calibration and registration have to be considered. IPS was evaluated for accuracy, robustness, and repeatability in a combined indoor and outdoor environment. The outstanding performance of the system is shown on a pedestrian navigation task.

Uncertainty Model for Template Feature Matching

Using visual odometry and inertial measurements, indoor and outdoor positioning systems can perform an accurate self-localization in unknown, unstructured environments where absolute positioning systems (e.g. GNSS) are unavailable. However, the achievable accuracy is highly affected by the residuals of calibration, the quality of the noise model, etc. Only if these unavoidable uncertainties of sensors and data processing can be taken into account and be handled via error propagation, which allows to propagate them through the entire system. The central filter (e.g. Kalman filter) of the system can then make use of the enhanced statistical model and use the propagated errors to calculate the optimal result. In this paper, we focus on the uncertaintiy calculation of the elementary part of the optical navigation, the template feature matcher. First of all, we propose a method to model the image noise. Then we use Taylor’s theorem to extend two very popular and efficient template feature matchers sum-of-absolute-differences (SAD) and normalized-cross-correlation (NCC) to get sub-pixel matching results. Based on the proposed noise model and the extended matcher, we propagate the image noise to the uncertainties of sub-pixel matching results. Although the SAD and NCC are used, the image noise model can be easily combined with other feature matchers. We evaluate our method by an Integrated Positioning System (IPS) which is developed by German Aerospace Center. The experimental results show that our method can improve the quality of the measured trajectory. Moreover, it increases the robustness of the system.

IPS – a vision aided navigation system

Abstract Ego localization is an important prerequisite for several scientific, commercial, and statutory tasks. Only by knowing one’s own position, can guidance be provided, inspections be executed, and autonomous vehicles be operated. Localization becomes challenging if satellite-based navigation systems are not available, or data quality is not sufficient. To overcome this problem, a team of the German Aerospace Center (DLR) developed a multi-sensor system based on the human head and its navigation sensors – the eyes and the vestibular system. This system is called integrated positioning system (IPS) and contains a stereo camera and an inertial measurement unit for determining an ego pose in six degrees of freedom in a local coordinate system. IPS is able to operate in real time and can be applied for indoor and outdoor scenarios without any external reference or prior knowledge. In this paper, the system and its key hardware and software components are introduced. The main issues during the development of such complex multi-sensor measurement systems are identified and discussed, and the performance of this technology is demonstrated. The developer team started from scratch and transfers this technology into a commercial product right now. The paper finishes with an outlook.