EKF-based Simultaneous Localization And Mapping Research Articles

Information-based view initialization in visual SLAM with a single omnidirectional camera

This paper presents a novel mechanism to initiate new views within the map building process for an EKF-based visual SLAM (Simultaneous Localization and Mapping) approach using omnidirectional images. In presence of non-linearities, the EKF is very likely to compromise the final estimation. Particularly, the omnidirectional observation model induces non-linear errors, thus it becomes a potential source of uncertainty. To deal with this issue we propose a novel mechanism for view initialization which accounts for information gain and losses more efficiently. The main outcome of this contribution is the reduction of the map uncertainty and thus the higher consistency of the final estimation. Its basis relies on a Gaussian Process to infer an information distribution model from sensor data. This model represents feature points existence probabilities and their information content analysis leads to the proposed view initialization scheme. To demonstrate the suitability and effectiveness of the approach we present a series of real data experiments conducted with a robot equipped with a camera sensor and map model solely based on omnidirectional views. The results reveal a beneficial reduction on the uncertainty but also on the error in the pose and the map estimate.

Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

A technique for optimizing the efficiency of the sub-map method for large-scale simultaneous localization and mapping (SLAM) is proposed. It optimizes the benefits of the sub-map technique to improve the accuracy and consistency of an extended Kalman filter (EKF)-based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing the sub-map technique in SLAM. Such issues include the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error-correcting process were engaged to optimize the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.

Dynamic EKF-based SLAM for autonomous mobile convergence platforms

This paper presents a new Simultaneous Localization and Mapping (SLAM) framework for solving the problem of SLAM in dynamic environments. The landmark location change causes the error of robot pose estimation and landmark mapping. In this paper, we propose the Dynamic Extended Kalman Filter (EKF) SLAM based on the independence of the dynamic landmarks. The proposed framework decomposes the SLAM problem into a traditional SLAM problem for the static landmarks and individual SLAM problems for the dynamic landmarks. Therefore, in the dynamic environments, it is able to minimize the error caused by the dynamic landmarks and reduce the uncertainty in the robot pose and the landmark locations. In order to validate the proposed approach, we implement an indoor mobile robot platform with a Red Green Blue - Depth (RGB-D) sensor and utilize Speeded Up Robust Features (SURF) algorithm to extract appearance-based features. The simulation and experimental results show the validity and robustness of the Dynamic EKF SLAM in indoor environments including the dynamic landmarks.

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.

Power-SLAM: a linear-complexity, anytime algorithm for SLAM

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.

GEOMETRIC TECHNIQUES FOR HUMANOID PERCEPTION

In this article, we propose a conformal model for 3D visual perception. In our model, the two views are fused in an extended 3D horopter model. For visual simultaneous localization and mapping (SLAM), an extended Kalman filter (EKF) technique is used for 3D reconstruction and determination of the robot head pose. In addition, the Viola and Jones machine-learning technique is applied to improve the robot relocalization. The 3D horopter, the EKF-based SLAM, and the Viola and Jones machine-learning technique are key elements for building a strong real-time perception system for robot humanoids. A variety of interesting experiments show the efficiency of our system for humanoid robot vision.

Towards features updating selection based on the covariance matrix of the SLAM system state

SUMMARYThis paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback–Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work.

Observability-based Rules for Designing Consistent EKF SLAM Estimators

In this work, we study the inconsistency problem of extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) from the perspective of observability. We analytically prove that when the Jacobians of the process and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has an observable subspace of dimension higher than that of the actual, non-linear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Based on these theoretical results, we propose a general framework for improving the consistency of EKF-based SLAM. In this framework, the EKF linearization points are selected in a way that ensures that the resulting linearized system model has an observable subspace of appropriate dimension. We describe two algorithms that are instances of this paradigm. In the first, termed observability constrained (OC)-EKF, the linearization points are selected so as to minimize their expected errors (i.e. the difference between the linearization point and the true state) under the observability constraints. In the second, the filter Jacobians are calculated using the first-ever available estimates for all state variables. This latter approach is termed first-estimates Jacobian (FEJ)-EKF. The proposed algorithms have been tested both in simulation and experimentally, and are shown to significantly outperform the standard EKF both in terms of accuracy and consistency.

Consistency Analysis of EKF-based SLAM by Measurement Noise and Observation Times

摘要: Inconsistency is a fundamental problem in simultaneous localization and mapping (SLAM). Previous works from predecessors have studied the inconsistent problem of extended Kalman filter (EKF) SLAM algorithm focusing on the linearization errors. In this paper, we studied the inconsistency issue of EKF SLAM in theory based on measurement noise and observation time. In a simplified situation, we deduced some useful theorems of estimated covariance matrix. Then, we made use of them to investigate the inconsistency issue. We showed that the measurement noise and the observation times can drive the EKF SLAM out of consistency. Moreover, we demonstrated the explicit effects of measurement noise and observation times on inconsistency of the EKF SLAM. Our simulation experiments verified the results. 关键词: Simultaneous localization and mapping (SLAM) / extended Kalman filter (EKF) / consistency / measurement noise / observation times

Consistency Analysis of EKF-based SLAM by Measurement Noise and Observation Times

Inconsistency is a fundamental problem in simultaneous localization and mapping (SLAM). Previous works from predecessors have studied the inconsistent problem of extended Kalman filter (EKF) SLAM algorithm focusing on the linearization errors. In this paper, we studied the inconsistency issue of EKF SLAM in theory based on measurement noise and observation time. In a simplified situation, we deduced some useful theorems of estimated covariance matrix. Then, we made use of them to investigate the inconsistency issue. We showed that the measurement noise and the observation times can drive the EKF SLAM out of consistency. Moreover, we demonstrated the explicit effects of measurement noise and observation times on inconsistency of the EKF SLAM. Our simulation experiments verified the results.

SLAM for robot navigation

Simultaneous Localization and Mapping (SLAM) for the mobile robot navigation has two main problems. The first problem is the computational complexity due to the growing state vector with the added landmark in the environment. The second problem is the data association which matches the observations and landmarks in the state vector. In this study, we compare the Extended Kalman Filter-(EKF)-based SLAM which is well-developed and well-known algorithm, and the Compressed Extended Kalman Filter-(CEKF)-based SLAM developed for decreasing the computational complexity of the EKF-based SLAM. We describe two simulation programs to investigate these techniques. The first program is written for the comparison of EKF- and CEKF-based SLAMS according to the computational complexity and covariance matrix error with the different numbers of landmarks. In the second program, EKF- and CEKF-based SLAM with the ICNN and JCBB data association algorithms simulations are presented. For this simulation, the differential drive vehicle that moves in a 10m square trajectory and LMS 200 2-D) laser range finder are modelled and landmarks are randomly scattered in that 10m square environment.