ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 133-137, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-4/133/2016/
doi:10.5194/isprs-annals-III-4-133-2016
 
03 Jun 2016
EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR
Hongmou Zhang, Jürgen Wohlfeil, and Denis Grießbach German Aerospace Center, Rutherfordstrae.2, 12489 Berlin, Germany
Keywords: Feature detector, Vision-aided inertial navigation, AGAST, IPS, KLT Abstract. Vision-aided inertial navigation is a navigation method which combines inertial navigation with computer vision techniques. It can provide a six degrees of freedom navigation solution from passive measurements without external referencing (e.g. GPS). Thus, it can operate in unknown environments without any prior knowledge. Such a system, called IPS (Integrated Positioning System) is developed by the German Aerospace Center (DLR).

For optical navigation applications, a reliable and efficient feature detector is a crucial component. With the publication of AGAST, a new feature detector has been presented, which is faster than other feature detectors. To apply AGAST to optical navigation applications, we propose several methods to improve its performance. Based on a new non-maximum suppression algorithm, automatic threshold adaption algorithm in combination with an image split method, the optimized AGAST provides higher reliability and efficiency than the original implementation using the Kanade Lucas Tomasi (KLT) feature detector. Finally, we compare the performance of the optimized AGAST with the KLT feature detector in the context of IPS. The presented approach is tested using real data from typical indoor scenes, evaluated on the accuracy of the navigation solution. The comparison demonstrates a significant performance improvement achieved by the optimized AGAST.

Conference paper (PDF, 1340 KB)


Citation: Zhang, H., Wohlfeil, J., and Grießbach, D.: EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 133-137, doi:10.5194/isprs-annals-III-4-133-2016, 2016.

BibTeX EndNote Reference Manager XML