Extended Kalman Filter Simultaneous Localization And Mapping Algorithm Research Articles

Implementation of Mobile Robot’s Navigation using SLAM based on Cloud Computing

This paper is concerned with the implementation of EKF-SLAM (Extended Kalman Filter- Simultaneous Localization and Mapping) algorithm using a cloud computing architecture based on ROS (Robot Operating System). The localization and mapping is essential step to navigate a mobile robot in unknown environment. The implemented EKF-SLAM has used a landmark that sensed using IR Emitter sensor provided by the Kinect camera to update a map of the environment and simultaneously estimate the robot’s position and orientation within the map. The implementation was done using three parts. The first one was the TurleBot Mobile robot with the Kinect camera, which was simulated in Gazebo environment. The second part was the EKF-SLAM running under the MATLAB to generate the Map and Location data. The third part was the ROS Master node, which runs on the cloud to enable part one and two to communicate using topics. The scan data from Kinect camera and the location data from the odometer is transferred from the first part to the second part through ROS Master node after impaired with zero mean Gaussian noise . Then the second part performs EKF-SLAM and transmit the corrections to the first part through the ROS Master node as well.

CI‐Graph simultaneous localization and mapping for three‐dimensional reconstruction of large and complex environments using a multicamera system

AbstractSubmapping and graphical methods have been shown to be valuable approaches to simultaneous localization and mapping (SLAM), providing significant advantages over the classical extended Kalman filter (EKF) solution: they are faster and, when using local coordinates, produce more consistent estimates. The main contribution of this paper is CI‐Graph SLAM, a novel algorithm that is able to efficiently map large environments by building a graph of submaps and a spanning tree of this graph with the following properties: (1) any pair of neighboring submaps in the spanning tree are conditionally independent and (2) the current submap is always up to date, containing the marginal probabilities of the submap variables given all previous measurements. Thanks to these properties, an old submap can be updated at any time by performing a single propagation from the current map to the old submap along the spanning tree. This operation is required only when a map is revisited, with a cost linear with the number of maps in the loop. At the end of the experiment the method performs a single propagation through the whole tree, recovering exactly the same marginals for all the map variables as the EKF–SLAM algorithm does, without ever needing to compute the whole covariance matrix. To evaluate CI‐Graph performance in extremely loopy environments, the method was tested using a synthetic Manhattan world. The behavior of the algorithm in large real environments is shown using the public data sets from the RAWSEEDS project in which a robot equipped with a trinocular camera traversed indoor and outdoor environments with several loops and revisited areas. Loops are robustly closed using a novel technique that detects candidate loop closures using a visual vocabulary tree and filters them using temporal and geometric constraints. Our experiments show that when using frontal cameras, the technique outperforms FAB‐MAP. The epipolar geometry of the loop‐closing images is used to find feature matches that are imposed on the CI‐Graph to correct the submap estimations along the loop. © 2010 Wiley Periodicals, Inc.

T–S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

This paper describes a Takagi–Sugeno (T–S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T–S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter–SLAM algorithm.

Divide and Conquer: EKF SLAM in $O(n)$

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we show that <emphasis emphasistype="italic">all</emphasis> processes associated with the move-sense-update cycle of extended Kalman filter (EKF) Simultaneous Localization and Mapping (SLAM) can be carried out in time <emphasis emphasistype="italic">linear</emphasis> with the number of map features. We describe <emphasis emphasistype="italic">Divide and Conquer</emphasis> SLAM, which is an EKF SLAM algorithm in which the computational complexity per step is reduced from <formula formulatype="inline"><tex Notation="TeX">$O(n^2)$</tex></formula> to <formula formulatype="inline"><tex Notation="TeX"> $O(n)$</tex></formula>, and the total cost of SLAM is reduced from <formula formulatype="inline"><tex Notation="TeX">$O(n^3)$</tex> </formula> to <formula formulatype="inline"><tex Notation="TeX">$O(n^2)$</tex></formula>. 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. </para>