— For the Simultaneous Localization and Mapping problem several efficient algorithms have been proposed that make use of a sparse information matrix representation (e.g. SEIF, T...
— The main contribution of this paper is a new SLAM algorithm for the mapping of large scale environments by combining local maps. The local maps can be generated by traditional ...
Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM sc...
— This paper provides a novel state vector and covariance sub-matrix recovery algorithm for a recently developed submap based exactly sparse Extended Information Filter (EIF) SLA...
Abstract — In [13], a new algorithm was proposed for efficiently solving the simultaneous localization and mapping (SLAM) problem. In this paper, we extend this algorithm to han...