Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations (original) (raw)
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Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network
Indian Journal of Science and Technology, 2015
A keyword requisite for a correctly autonomous robot is that it can localize itself and carefully map its surroundings simultaneously 1. Many algorithms have been offered for solving SLAM obstacles, for example particle filter, Kalman Filter (KF), Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). The UKF SLAM making a Gaussian noise supposition for the robot observation and its movement. The UKF encourage calculation values analogous to the ones of EKF but does not requirement the linearization of the fundamental model 2. The UKF uses the unscented transform to linearize the movement and measurement models 3. RBF, adaptive to the change of environmental data flowing through the network during the process, can be combined with an UKF to atone for some of the disadvantages of an UKF SLAM method 4. Amir Panah 5 solved the SLAM problem with a neural network based on an Unscented Kalman filter. According to the research results, the UKF SLAM based on Neural Network, shows better performance than the Standard UKF SLAM. Stubberud et al. 6 extended an adaptive EKF composed with neural networks, with a neuro-observer to learn system uncertainties on-line. The offered system boost the performance of a control system comprise uncertainties in the state-estimator's model. In this article, we describe a Hybrid way using RBF network and UKF based SLAM for decline uncertainty in compare to SLAM using Standard UKF. we do, consider the power of UKF based RBF algorithm to handle nonlinear attributes of a mobile robot. We started by introducing some related algorithms on SLAM are explained in section 2, and the Hybrid algorithm is presented in section 3. The detailed description of the simulation and experimental results is shown in Section 4 and finally in Section 5 summarizes the results and gives an outlook on future research activities.
Fast and accurate SLAM with Rao–Blackwellized particle filters
Robotics and Autonomous Systems, 2007
Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters.
On Simulation and Analysis of Mobile Robot SLAM using Rao-Blackwellized Particle Filters
The simultaneous localization and mapping (SLAM) is considered as a crucial prerequisite for purely autonomous mobile robots. In this paper, we demonstrate the mobile robot SLAM using Rao-Blackwellized particle filters (RBPF) through computer simulations under MATLAB platform, while an analytical investigation into the involved algorithms is presented. Then we make further comparisons, not only in parallel between the FastSLAM 1.0 and FastSLAM 2.0, also in vertical between FastSLAM performance and EKF-SLAM performance which used to be the dominant approach to the SLAM problem. Vivid simulations and numerical analysis make the paper illustrated with clarity and perception.
An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM
Journal of Intelligent & Robotic Systems, 2019
Extended Kalman Filter (EKF) has been popularly utilized for solving Simultaneous Localization and Mapping (SLAM) problem. Essentially, it requires the accurate system model and known noise statistic. Nevertheless, this condition can be satisfied in simulation case. Hence, EKF has to be enhanced when it is applied in the real-application. Mainly, this improvement is known as adaptive-based approach. In many different cases, it is indicated by some manners of estimating for either part or full noise statistic. This paper present a proposed method based on the adaptive-based solution used for improving classical EKF namely An Adaptive Extended Kalman Filter. Initially, the classical EKF was improved based on Maximum Likelihood Estimation (MLE) and Expectation-Maximization (EM) Creation. It aims to equips the conventional EKF with ability of approximating noise statistic and its covariance matrices recursively. Moreover, EKF was modified and improved to tune the estimated values given by MLE and EM creation. Besides that, the recursive noise statistic estimators were also estimated based on the unbiased estimation. Although it results high quality solution but it is followed with some risks of non-positive definite matrices of the process and measurement noise statistic covariances. Thus, an addition of Innovation Covariance Estimation (ICE) was also utilized to depress this possibilities. The proposed method is applied for solving SLAM problem of autonomous wheeled mobile robot. Henceforth, it is termed as AEKF-SLAM Algorithm. In order to validate the effectiveness of proposed method, some different SLAM-Based algorithm were compared and analyzed. The different simulation has been showing that the proposed method has better stability and accuracy compared to the conventional filter in term of Root Mean Square Error (RMSE) of Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).
SLAM in O(log n) with the Combined Kalman-Information Filter
2010
In this paper1 we describe the Combined Kalman-Information Filter SLAM algorithm (CF SLAM), a judicious combination of Extended Kalman (EKF) and Extended Information Filters (EIF) that can be used to execute highly efficient SLAM in large environments. CF SLAM is always more efficient than any other EKF or EIF algorithm: filter updates can be executed in as low as O (logn) as compared with O (n2) for Map Joining SLAM, O (n) for Divide and Conquer (D&C) SLAM, and the Sparse Local Submap Joining Filter (SLSJF).
Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005
Recently Rao-Blackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper we present adaptive techniques to reduce the number of particles in a Rao-Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decrease the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out re-sampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches.
Journal of Intelligent and Robotic Systems, 2009
The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.
A Review: Simultaneous Localization and Mapping Algorithms
Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this field by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule.
Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction
21st Mediterranean Conference on Control and Automation (MED 2013), 2013
In this work we compare the performance of two algorithms, respectively based on the Extended Kalman Filter and the Unscented Kalman Filter, for the mobile robot localization and environment reconstruction problem. The proposed algorithms do not require any assumption on the robot working space: they are driven only by the measurements taken using ultrasonic sensors located onboard the robot. We also devise a switching sensors activation policy, which allows energy saving still achieving accurate tracking and reliable mapping of the workspace. The results show that the two filters work comparably well, in spite of the superior theoretical properties of the Unscented Filter.