Image Moments for Higher-Level Feature Based Navigation
2013 (English)In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, 602-609 p.Conference paper (Refereed)
This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).
Place, publisher, year, edition, pages
2013. 602-609 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
IdentifiersURN: urn:nbn:se:kth:diva-124371DOI: 10.1109/IROS.2013.6696413ScopusID: 2-s2.0-84893787281ISBN: 978-146736358-7OAI: oai:DiVA.org:kth-124371DiVA: diva2:634379
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013; Tokyo, Japan, 3-8 November, 2013
QC 201402282013-06-302013-06-302014-02-28Bibliographically approved