Extended Kalman Filter Simultaneous Localization And Mapping Research Articles

Developing a real time navigation for the mobile robots at unknown environments

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

Guidance, Navigation, and Control System for Landing near Plume Source on Enceladus

Saturn’s moon Enceladus is one of the most promising candidates in the solar system for hosting microbial life. Plumes emanating from its south pole are ejecting water directly from the ocean to space. The Enceladus Explorer (EnEx) lander mission concept aims to land near a plume source and deploy a melting probe to sample liquid pockets under the plumes for life. Landing there would be exceptionally challenging due to the rough canyonous topography, polar lighting conditions, a surface covered by superfine snow, and strict planetary protection regulations. In this work the Guidance, Navigation, and Control (GN&C) system and operations for the critical approach phase of such a landing are investigated. An advanced GN&C system is defined, comprising extended Kalman filter simultaneous localization and mapping (EKF-SLAM)–based navigation, fuzzy-reasoning-based hazard detection and avoidance, and convex guidance. To verify the landing feasibility and further refine the concept, the system is investigated using a closed-loop simulation tool we created. The performance of each function is analyzed separately with sensitivity and worst-case analyses. A Monte Carlo simulation encompassing all functions is performed to estimate mission success statistics.

General Learning Modeling for AUV Position Tracking

In this article, we propose a nonlinear model based on hidden-layer neural networks and local Gaussian process regression. The hidden-layer neural networks have short training time, whereas the local Gaussian process regression is more suitable for nonlinearity. According to the abovementioned advantages of hidden-layer neural network and local Gaussian process regression, we get the measurement learning model and global process learning model offline and local process learning model online. Then, the proposed learning-based models have been applied to extended Kalman filter-simultaneous localization and mapping (EKF-SLAM), Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping (FastSLAM), and unscented Kalman filter--simultaneous localization and mapping (UKF-SLAM) for autonomous underwater vehicles position tracking with field experiments. Compared with the conventional process and measurement models, improved position tracking performance can be achieved without cumbersome system analysis nor identification, benefiting from the proposed model. Experiments in more than 3883-m run show that the root-mean-square error has been improved by 36.95%, 37.14%, and 27.65% in EKF-SLAM, FastSLAM, and UKF-SLAM frameworks, respectively.

Hybrid SLAM modelling of autonomous robot with augmented reality device

This paper would focus on an innovative approach in simultaneous localization and mapping (SLAM) for autonomous robot navigation based on augmented reality (AR) technology. HoloLens as an advanced AR device using several innovative technologies, is selected for this Hybrid SLAM approach. The paper reveals that how this device can be used in advanced control for autonomous robot positioning, mapping and trajectory following. The HoloLens’ spatial mapping feature simplifies the SLAM problem considering as a hybrid algorithm combined with conventional EKF (Extended Kalman Filter). To convey the proposed hybrid approach, first a simulation model is established through EKF SLAM. second spatial mapping classes are introduced and applied in HoloLens. The result would illustrate the achieved map of environment by this algorithm.

Compensation of dynamic electromagnetic field distortion using simultaneous localization and mapping method with application in endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA) guidance.

Electromagnetic (EM)-based navigation methods without line-of-sight restrictions may improve lymph node sampling precision in transbronchial needle aspiration (TBNA) procedure. However, EM tracking susceptibility to metallic objects severely declines its precision. We proposed to track the location of a tool in a dynamic bronchial phantom and compensate field distortion in a real-time procedure. Extended Kalman filter simultaneous localization and mapping (EKF-SLAM) algorithm employ the bronchial motion and observations of a redundant sensor. The proposed approach was applied to the phantom with four different amplitudes of breathing motion in the presence of two types of field-distorting objects. The proposed approach improved the accuracy of EM tracking on average from 18.94±1.17mm to 4.59±0.29mm and from 14.2±0.69mm to 4.31±0.18mm in the presence of steel and aluminum, respectively. With EM tracking position error reduction based on the EKF-SLAM technique, the approach is appeared promising for a navigated ultrasound TBNA procedure.

Exploration of Designed Robot in Non Linear Movement Using EKF SLAM and Its Stability on Loose Concrete Surface

The paper analyze the pressure exerted during movement of robot on path (marble flooring and Pebbled surface) using Gmapping based EKF (Extended Kalman Filter) for SLAM (Simultaneous Localization and Mapping). The experiments were carried out using meta software (vMicro) and Arduino BSP on.Net framework, which was not discussed in collaborative SLAM. In this paper, pressure and physical information captured by various sensors are analyzed and concludes robot’s behavior at different surface type with various angles.

A novel edge-enabled SLAM solution using projected depth image information

Environmental mapping is the key step for mobile robots to perform tasks independently and perfectly. In recent years, visual SLAM, laser-based SLAM and simultaneous localization and mapping (SLAM) have aroused the interest of many people. Unfortunately, those technologies are not widely used, limited by the computational complexity, data processing and very low and predictable latency. This paper had mainly completed the following work and edge-enabled computing-based edge computing (Shi and Dustdar in Computer 49(5):78–81, 2016) is used as a solution to accelerate calculation. First of all, this research design works with inertial unit mobile robotic navigation systems, and all sensors are connected in edge layers in the framework of edge computing and explore the accelerometer, electronic compass, and gyroscope data. The accelerometer data are integrated using the Kalman filter data fusion algorithm to filter the random drift error caused by the gyroscope and the electronic compass. The state of the machine is determined by calculation of the corresponding attitude angle and position information. Second, a low-cost distance sensor is used to detect the depth and upload to the other fog node for computation. Next, the 3D point coordinate information is projected onto the two-dimensional coordinate extraction feature point to establish the feature map. Third, the extended Kalman filter SLAM is used to achieve simultaneous positioning and mapping. Finally, the method is validated in the experiment, proving that the method is feasible. The main improvement in this article is as follows: First, the multi-sensor data fusion algorithm is used to reduce the positioning error. Second, we use low-cost distance sensors to measure the depth of the model environment and reduce the cost. Third, we would take advantage of translating the three-dimensional depth information into a flat two-dimensional projection information to reduce the calculation of load and computing time. Fourth, our computation is distributed in different layers and focuses on edge-enabled platform to decrease the latency and redundancy.

A two-level optimized graph-based simultaneous localization and mapping algorithm

PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.

Indoor SLAM application using geometric and ICP matching methods based on line features

This study presents an autonomous guided vehicle (AGV) with simultaneous localization and map building (SLAM) based on matching method, and extended Kalman filter SLAM. In general, the AGV is a large mobile robot that is used in transportation to carry cargoes, and it is guided using wired or wireless guidance systems. The guidance system based AGV accounts for a majority of robots in the mobile robot industry. However, in semiconductor factories, landmarks are unavailable; hence, the existing system has not been used in the mentioned environments. Therefore, the SLAM technology is applied in the environments, and can guide the AGV without landmarks. However, the accuracy of the SLAM can be low owing to measurement error of sensors and a cumulative calculation caused by localization sensors. Therefore, the accuracy is frequently assumed to be incorrect; moreover, the accuracy of the built map is low. In order to solve the problems, this study proposes the AGV with the SLAM based on matching methods; two matching method; geometric matching method and iterative closest point algorithm. The performance of the proposed method is compared with typical methods such as singular value decomposition / RIGID transformation based technologies using feature-point-based SLAM and is compared with the aforementioned two methods using the extended Kalman filter SLAM. The proposed method is more efficient than the typical methods used in the comparison.

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.

An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics

This paper is intended to pave the way for new researchers in the field of robotics and autonomous systems, particularly those who are interested in robot localization and mapping. We discuss the fundamentals of robot navigation requirements and provide a review of the state of the art techniques that form the bases of established solutions for mobile robots localization and mapping. The topics we discuss range from basic localization techniques such as wheel odometry and dead reckoning, to the more advance Visual Odometry (VO) and Simultaneous Localization and Mapping (SLAM) techniques. We discuss VO in both monocular and stereo vision systems using feature matching/tracking and optical flow techniques. We discuss and compare the basics of most common SLAM methods such as the Extended Kalman Filter SLAM (EKF-SLAM), Particle Filter and the most recent RGB-D SLAM. We also provide techniques that form the building blocks to those methods such as feature extraction (i.e. SIFT, SURF, FAST), feature matching, outlier removal and data association techniques.

Ellipsoid SLAM: a novel set membership method for simultaneous localization and mapping

The extended Kalman filter (EKF) simultaneous localization and mapping (SLAM) requires the uncertainty to be Gaussian noise. This assumption can be relaxed to bounded noise by the set membership SLAM. However, the published set membership SLAMs are not suitable for large-scale and online problems. In this paper, we use ellipsoid algorithm for solving SLAM problem. The proposed ellipsoid SLAM has advantages over EKF SLAM and the other set membership SLAMs, in noise condition, online realization, and large-scale problem. By bounded ellipsoid technique, we analyze the convergence and stability of the ellipsoid SLAM. Simulation and experimental results show that the proposed ellipsoid SLAM is effective for online and large-scale problems such as Victoria Park dataset.

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.

An adaptive scheme for robot localization and mapping with dynamically configurable inter-beacon range measurements.

This work is motivated by robot-sensor network cooperation techniques where sensor nodes (beacons) are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). This paper presents a RO-SLAM scheme that actuates over the measurement gathering process using mechanisms that dynamically modify the rate and variety of measurements that are integrated in the SLAM filter. It includes a measurement gathering module that can be configured to collect direct robot-beacon and inter-beacon measurements with different inter-beacon depth levels and at different rates. It also includes a supervision module that monitors the SLAM performance and dynamically selects the measurement gathering configuration balancing SLAM accuracy and resource consumption. The proposed scheme has been applied to an extended Kalman filter SLAM with auxiliary particle filters for beacon initialization (PF-EKF SLAM) and validated with experiments performed in the CONET Integrated Testbed. It achieved lower map and robot errors (34% and 14%, respectively) than traditional methods with a lower computational burden (16%) and similar beacon energy consumption.

Robot mapping using local invariant feature detectors

Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach – SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings – Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value – The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

Calibration of RGB-D Sensors for Robot SLAM

This paper presents a calibration procedure for a Kinect RGB-D sensor and its application to robot simultaneous localization and mapping(SLAM). The calibration procedure consists of two stages: in the first stage, the RGB image is aligned with the depth image by using the bilinear interpolation. The distorted RGB image is further corrected in the second stage. The calibrated RGB-D sensor is used as the sensing device for robot navigation in unknown environment. In SLAM tasks, the speeded-up robust features (SURF) are detected from the RGB image and used as landmarks in the environment map. The depth image could provide the stereo information of each landmark. Meanwhile, the robot estimates its own state and landmark locations by mean of the Extended Kalman filter (EKF). The EKF SLAM has been carried out in the paper and the experimental results showed that the Kinect sensors could provide the mobile robot reliable measurement information when navigating in unknown environment.

Improvement of speeded-up robust features for robot visual simultaneous localization and mapping

SUMMARYA robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.

An Inter-Vehicle Observations Approach to Cooperative SLAM

In this paper, a cooperative simultaneous localization and mapping (CSLAM) approach is proposed. The main idea of the proposed approach is to initialize the map of each vehicle comprising the known initial positions of collaborating vehicles, and continuing the SLAM process considering these vehicles as dynamic features (landmarks). The inter-vehicle observations and shared control data are used to predict and update the dynamic features. The proposed approach is first elucidated mathematically based on extended Kalman filter-simultaneous localization and mapping (EKF-SLAM) solution, and then, the performance gain is evaluated using simulations conducted for unmanned surface vehicles (USVs) with radar sensors. Simulation results show that this approach provides noticeable improvement versus the single-vehicle SLAM (or Mono-SLAM) in terms of localization accuracy and mapping performance.

Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features

An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.

Fuzzy adaptive extended Kalman filter SLAM algorithm based on the improved wild geese PSO algorithm

Because the problem of the extended Kalman filter localization and mapping algorithm priori noise model is difficult to manage, this paper proposes an improved wild geese particle swarm algorithm based on the fuzzy adaptive Kalman filter localization and mapping algorithm. We take advantage of the the fractional calculus to improve particle speed of evolution, and make use of chaos to improve the initialization of the particle and the precocious one when processing. The improvement of wild geese particle swarm algorithm is shown in convergence rate and avoiding premature, then they can improve geese particle swarm algorithm for fuzzy adaptive extended Kalman filter localization and mapping algorithm training. in contrast with geese particle swarm algorithm fuzzy adaptive extended Kalman filter simultaneous localization and mapping algorithm, the new algorithm positioning and composition has greatly improved.