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Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding...
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) SLAM algorithm - sparse local submap joining filter (SLSJF). The algorithm achieves exact recovery instead of approximate recovery. The recovery algorithm is very efficient because of an incremental Cholesky factorization approach...
This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and...
This paper presents an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem with the robot initial locations completely unknown. Each robot builds its own local map using the traditional extended Kalman filter (EKF) SLAM algorithm. We provide a new method to fuse the local maps into a jointly maintained global map by first transforming the local map state estimate into...
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 extended Kalman filter (EKF) based SLAM. Relationships between the locations of the landmarks in the local map are then extracted and used in an extended information filter (EIF) to build a global map. An important feature is...
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