Fast and Reliable Vision-Based Navigation for Real Time Kinematic Applications

Abstract

Abstract: Automatic Image Matching (AIM) is the term used to identify the automatic detection of corresponding points located on the overlapping areas of multiple images. AIM is extensively used with Mobile Mapping System (MMS) for different engineering applications, such as highway infrastructure mapping, monitoring of road surface quality and markings, telecommunication, emergency response, and collecting data for Geographical Information Systems (GIS). Robotics community and Simultaneous Localization And Mapping (SLAM) based applications are other important areas that require fact and welldistributed AIM for robust vision navigation solutions. Different robust feature detection methods are commonly used for AIM, such as Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). The performance of such techniques have been widely investigated and compared, showing high capability to provide reliable and precise results. However, these techniques are still limited to be used for real and nearly real time SLAM based applications, such as intelligent Robots and low-cost Unmanned Aircraft Vehicles (UAV) based on vision navigation. The main limitations of these AIM techniques are represented in the relatively long processing time and the random distribution of matched points over the common area between images. This paper works on overcoming these two limitations, providing extremely fast AIM with well- distributed common points for robust real time vision navigation. Digital image pyramid, Epipolar line and 2D transformation have been utilized for limiting the size of search windows significantly and determining the rotating angle and scale level of features, reducing the overall processing time considerably. Using limited number of well-distributed common points has also helped to speed up the automatic matching besides providing robust vision navigation solution. The idea has been tested with terrestrial MMS images, and surveying UAV aerial images. The results reflect the high capability of the followed technique in providing fast and robust AIM for real-time SLAM based applications. Keywords: Automatic Image Matching, Epipolar Line, Image Pyramid, SLAM, Vision Navigation, Real Time, Vision Navigation.