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). <br><br> 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.
Highlights
IPS was developed for real-time vision-aided inertial navigation (Grießbach, 2014), especially under conditions where external referencing is not available, such as for indoor environments, underground, etc
In order to improve the accuracy and the real-time processing ability of IPS, we explore to replace old Kanade Lucas Tomasi (KLT) feature detector working inside IPS with the new AGAST (Mair et al, 2010) feature detector
AGAST adopts the non-maximum suppression algorithm inherited from from Accelerated Segment Test (FAST), using a 3×3 square mask sliding over all features, and suppress low rating features in neighborhood area (Rosten and Drummond, 2006)
Summary
IPS was developed for real-time vision-aided inertial navigation (Grießbach, 2014), especially under conditions where external referencing is not available, such as for indoor environments, underground, etc. It has been s√hown that IPS can output the trajectory accuracy of about 2m/ h; that means, for our test database with 410 meters track (about 6.8 minutes), the 3D error is about 0.65 meter (Grießbach et al, 2014). Because of the demand for real-time processing based on poor computational resources, many feature detectors cannot meet the requirements needed for optical navigation
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