Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Nonlinear constraint network optimization for efficient map learning
IEEE Transactions on Intelligent Transportation Systems
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
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This manuscript addresses the problem of optimization- based Simultaneous Localization and Mapping (SLAM), which is of concern when a robot, traveling in an unknown environment, has to build a world model, exploiting sensor measurements. Although the optimization problem underlying SLAM is nonlinear and nonconvex, related work showed that it is possible to compute an accurate linear approximation of the optimal solution for the case in which measurement covariance matrices have a block diagonal structure. In this paper we relax this hypothesis on the structure of measurement covariance and we propose a linear approximation that can deal with the general unstructured case. After presenting our theoretical derivation, we report an experimental evaluation of the proposed technique. The outcome confirms that the technique has remarkable advantages over state-of-the-art approaches and it is a promising solution for large-scale mapping.