A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
D-SLAM: A Decoupled Solution to Simultaneous Localization and Mapping
International Journal of Robotics Research
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Sparse Local Submap Joining Filter for Building Large-Scale Maps
IEEE Transactions on Robotics
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This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques.The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm is built on the D-SLAM mapping algorithm (Wang et al. in Int. J. Robot. Res. 26(2):187---204, 2007) and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ efficiency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features.The paper proposes metrics for quantifying the performance of different mapping algorithms focusing on evaluating their consistency, accuracy and efficiency. The I-DMJ algorithm and a number of existing SLAM algorithms are evaluated using the proposed metrics. The simulation data sets and a preprocessed Victoria Park data set used in this paper are made available to enable interested researchers to compare their mapping algorithms with I-DMJ.