Sergey Zuev

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.

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.

Three-Frame Algorithm of Car Path Reconstruction from Airborne Traffic Data

The airborne traffic monitoring system forms a novel technology of detecting vehicle motion. An optical digital camera located on an airborne platform produces a series of images which then are processed to recognized the fixed vehicles. In this way the video data are converted into the time sequence of frames containing the vehicle coordinates. In the present work a three-frame algorithm is developed to identify the succeeding vehicle positions. It is based on finding the neighboring points in the frame sequence characterized by minimal acceleration. To verify and optimize the developed algorithm a “Virtual Road” simulator was created. Finally available empirical data are analyzed using the created algorithm.