A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Observability-based Rules for Designing Consistent EKF SLAM Estimators
International Journal of Robotics Research
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
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In this paper, we present an extended Kalman filter (EKF)-based estimator for simultaneous localization and mapping (SLAM) with processing requirements that are linear in the number of features in the map. The proposed algorithm, called the Power-SLAM, is based on three key ideas. Firstly, by introducing the Global Map Postponement method, approximations necessary for ensuring linear computational complexity of EKF-based SLAM are delayed over multiple time steps. Then by employing the Power Method, only the most informative of the Kalman vectors, generated during the postponement phase, are retained for updating the covariance matrix. This ensures that the information loss during each approximation epoch is minimized. Next, linear-complexity, rank-2 updates, that minimize the trace of the covariance matrix, are employed to increase the speed of convergence of the estimator. The resulting estimator, in addition to being conservative as compared to the standard EKF, has processing requirements that can be adjusted depending on the availability of computational resources. Lastly, simulation and experimental results are presented that demonstrate the accuracy of the proposed algorithm (Power-SLAM) when compared to the standard EKF-based SLAM with quadratic computational cost and two linear-complexity competing alternatives.