Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Consistent, convergent, and constant-time SLAM
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
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
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
SLAM in O(logn) with the Combined Kalman-Information Filter
Robotics and Autonomous Systems
Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain
Robotics and Autonomous Systems
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In this paper we show that SLAM can be executed in as low as O(log n) per step. Our algorithm, the Combined Filter SLAM, uses a combination of Extended Kalman and Extended Information filters in such a way that the total cost of building a map can be reduced to O(n log n), as compared with O(n3) for standard EKF SLAM, and O(n2) for Divide and Conquer (D&C) SLAM and the Sparse Local Submap Joining Filter (SLSJF). We discuss the computational improvements that have been proposed for Kalman and Information filters, discuss the advantages and limitations of each, and how a judicious combination results in the possibility of reducing the computational cost per step to O(log n).We use simulations and real datasets to show the advantages of the proposed algorithm