SLAM in O(log n) with the combined Kalman - information filter
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
1-point RANSAC for EKF-based structure from motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Information-based compact pose SLAM
IEEE Transactions on Robotics
International Journal of Robotics Research
Study of parameterizations for the rigid body transformations of the scan registration problem
Computer Vision and Image Understanding
SLAM in O(logn) with the Combined Kalman-Information Filter
Robotics and Autonomous Systems
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
Robotics and Autonomous Systems
Power-SLAM: a linear-complexity, anytime algorithm for SLAM
International Journal of Robotics Research
Linear-time robot localization and pose tracking using matching signatures
Robotics and Autonomous Systems
Real-time optical SLAM-based mosaicking for unmanned underwater vehicles
Intelligent Service Robotics
Monocular SLAM with undelayed initialization for an indoor robot
Robotics and Autonomous Systems
A review of path planning and mapping technologies for autonomous mobile robot systems
Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
Jointly compatible pair linking for visual tracking with probabilistic priors
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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In this paper, we show that all processes associated with the move-sense-update cycle of extended Kalman filter (EKF) Simultaneous Localization and Mapping (SLAM) can be carried out in time linear with the number of map features. We describe Divide and Conquer SLAM, which is an EKF SLAM algorithm in which the computational complexity per step is reduced from O(n 2) to O(n), and the total cost of SLAM is reduced from O(n 3) to O(n 2). Unlike many current large-scale EKF SLAM techniques, this algorithm computes a solution without relying on approximations or simplifications (other than linearizations) to reduce computational complexity. Also, estimates and covariances are available when needed by data association without any further computation. Furthermore, as the method works most of the time in local maps, where angular errors remain small, the effect of linearization errors is limited. The resulting vehicle and map estimates are more precise than those obtained with standard EKF SLAM. The errors with respect to the true value are smaller, and the computed state covariance is consistent with the real error in the estimation. Both simulated experiments and the Victoria Park dataset are used to provide evidence of the advantages of this algorithm.