ALBERT

Description: Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

Description: A conceptual approach to digital photogrammetry is presented. Automation of photogrammetric processes on digital photogrammetric workstations is considered with particular attention given to the automatic orientation and the surface reconstruction module. It is suggested that major progress toward autonomous softcopy workstations depends more on advances on the conceptual level rather than on refinement of system components such as hardware and algorithms.

Description: A new approach to reconstruct the 3D surface of the object space from digital images is described. All the object points obtained by an automatic orientation procedure lead to a first approximation of the surface. Edges are computed for one image and matched to the other image by grey level correlation or least-squares matching through the scale space. To every discrete step in the scale space there exists the digital stereopair (image pyramid), the corresponding surface digital elevation model (DEM), and the warped images. The warped images in this discrete scale space representation correspond to digital orthophotos obtained from the DEMs that result from matching the image pyramid. The warped images will be used on every successive level in the image pyramid in order to reduce the foreshortening problems associated with any area-based matching method.

Description: An examination is conducted of the feasibility of searching targets in aerial photographs by means of a parallel associative memory (PAM) that is based on the nearest-neighbor algorithm; the Hamming distance is used as a measure of closeness, in order to discriminate patterns. Attention has been given to targets typically used for ground-control points. The method developed sorts out approximate target positions where precise localizations are needed, in the course of the data-acquisition process. The majority of control points in different images were correctly identified.

Description: The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.