Paper Info
Title
Real-Time Visual SLAM with Resilience to Erratic Motion
Abstract
Simultaneous localisation and mapping using a single camera becomes difficult when erratic motions violate predictive motion models. This problem needs to be addressed when visual SLAM algorithms are transferred from robots or mobile vehicles onto hand-held or wearable devices. In this paper we describe a novel SLAM extension to a camera localisation algorithm based on particle filtering which provides resilience to erratic motion. The mapping component is based on auxiliary unscented Kalman filters coupled to the main particle filter via measurement covariances. This coupling allows the system to survive unpredictable motions such as camera shake, and enables a return to full SLAM operation once normal motion resumes. We present results demonstrating the effectiveness of the approach when operating within a desktop environment.
Year
DOI
Venue
2006
10.1109/CVPR.2006.240
CVPR (1)
Keywords
Field
DocType
predictive motion model,camera localisation algorithm,normal motion resumes,unpredictable motion,single camera,camera shake,real-time visual slam,erratic motion,novel slam extension,visual slam algorithm,full slam operation,unscented kalman filter,particle filter,particle filters,predictive models,mobile robots,simultaneous localization and mapping,resilience
Computer vision,Shake,Coupling,Computer science,Particle filter,Kalman filter,Artificial intelligence,Wearable technology,Robot,Simultaneous localization and mapping,Mobile robot
Conference
ISBN
Citations 
PageRank 
0-7695-2597-0
21
1.72
References 
Authors
8
2
Name
Order
Citations
PageRank
Mark Pupilli121217.39
Andrew Calway264554.66