Paper Info
Title
Bearing-Only Sam Using A Minimal Inverse Depth Parametrization Application To Omnidirectional Slam
Abstract
Safe and efficient navigation in large-scale unknown environments remains a key problem which has to be solved to improve the autonomy of mobile robots. SLAM methods can bring the map of the world and the trajectory of the robot. Monucular visual SLAM is a difficult problem. Currently, it is solved with an Extended Kalman Filter (EKF) using the inverse depth parametrization. However, it is now well known that the EKF-SLAM become inconsistent when dealing with large scale environments. Moreover, the classical inverse depth parametrization is over-parametrized, which can also be a cause of inconsistency. In this paper, we propose to adapt the inverse depth representation to the more robust context of smoothing and mapping (SAM). We show that our algorithm is not over-parameterized and that it gives very accurate results on real data.
Year
Venue
Keywords
2010
ICINCO
Simultaneous localization and mapping (SLAM), Smoothing and mapping (SAM), Extended Kalman filter (EKF), Bearing-only, Inverse depth representation
Field
DocType
Citations 
Inverse,Extended Kalman filter,Mathematical optimization,Parametrization,Control theory,Algorithm,Smoothing,Engineering,Simultaneous localization and mapping,Robot,Mobile robot,Trajectory
Conference
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Cyril Joly134.46
Patrick Rives225417.77