Recent advances in real-time visual SLAM have been based primarily on mapping isolated 3-D points. This presents difficulties when seeking to extend operation to wide areas, as the system state becomes large, requiring increasing computational effort. In this paper we present a novel approach to this problem in which planar structural components are embedded within the state to represent mapped points lying on a common plane. This collapses the state size, reducing computation and improving scalability, as well as giving a higher level scene description. Critically, the plane parameters are augmented into the SLAM state in a proper fashion, maintaining inherent uncertainties via a full covariance representation. Results for simulated data and for real-time operation demonstrate that the approach is effective.
|Translated title of the contribution||Discovering Planes and Collapsing the State Space in Visual SLAM|
|Title of host publication||18th British Machine Vision Conference|
|Publication status||Published - Sep 2007|
Bibliographical noteConference Proceedings/Title of Journal: Proceedings of the 18th British Machine Vision Conference
Conference Organiser: British Machine Vision Association