Visual Odometry using motion vectors from visual feature points

Marouane, Chadly; Maier, Marco; Leupold, Alexander und Linnhoff-Popien, Claudia (2016): Visual Odometry using motion vectors from visual feature points. 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcalá de Henares, Madrid, Spain, 4-7 October 2016. IEEE Computer Soc..

Abstract

In recent years, location-based services and indoor positioning systems gained increasing importance for both, research and industry. Visual localization systems have the advantage of not being dependent on dedicated infrastructure and thus are especially interesting for navigation within buildings. While there are already approaches of using pre-recorded databases of reference images to obtain an absolute position for a given query image, suitable means to estimate the relative movement of pedestrians from an ego perspective video are still missing. This paper presents a novel visual odometry system for pedestrians. The user carries a mobile device while walking - the camera aims into the direction of walking. Using only the video stream as input, the system generates a two-dimensional trajectory, which describes the path traveled by the user. Both, the user's current heading as well as the walking direction are estimated based on the movement of visual feature points in successive video frames. In order to assess the accuracy of the system, it is evaluated in three different scenarios (indoors in an university building, in an urban area and in a city park). Not relying on reference points (for instance provided by a database, which references visual feature points with geo-data), the error accumulates with distance traveled. After a walked distance of 100 meters, the average error lies between 4.6 and 13.9 meters (depending on the scenario). Consequently, the system is a promising approach for visual odometry, which can be used in conjunction with existing absolute visual positioning systems or as a core part of a future SLAM (simultaneous localization and mapping) system.

• Dokumententyp: Konferenzbeitrag (Bericht)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISSN: 2162-7347
• Sprache: Englisch