Authors:
Cyril Joly
and
Patrick Rives
Affiliation:
INRIA Sophia Antipolis Méditerranée, France
Keyword(s):
Simultaneous localization and mapping (SLAM), Smoothing and mapping (SAM), Extended Kalman filter (EKF), Bearing-only, Inverse depth representation.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
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 SLAMis 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 EKFSLAMbecome 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.