EKF-SLAM Algorithm Research Articles

Research on Obstacle Avoidance in Automatic Driving Based on SLAM Path Planning

Automatic driving based on SLAM is known as one of the key research topics today. At present, researchers have made progress in some aspects such as EKF-SLAM and Graph SLAM and some even have commercialized attempts. However, the problem of automatic driving obstacle avoidance based on SLAM still lacks a unified cognition. Therefore, the research topic of this paper is the method of obstacle avoidance in automatic driving based on SLAM trajectory planning. This paper summarizes and discusses the relevant research results by integrating relevant literature and further expounds the application of SLAM technology in automatic driving obstacle avoidance: The front-end vision based on feature point method is more mature than other front-end tracking options. EKF-SLAM algorithm is not suitable for complex large-scale maps. Image optimization SLAM is dedicated to obtaining the optimal pose, but it needs to be initialized according to the environment. Through the summary and discussion of the research results, this paper not only shows the application value of SLAM in automatic driving obstacle avoidance, but also gives readers a reference for the choice of SLAM technology

KISS-Keep It Static SLAMMOT-The Cost of Integrating Moving Object Tracking into an EKF-SLAM Algorithm.

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety-distance-error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety-distance-error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms.

A New Method for Optimizing the Consistency of EKF-SLAM Algorithm Based on Multi-Dimensional Taylor-Net Strategy

A New Method for Optimizing the Consistency of EKF-SLAM Algorithm Based on Multi-Dimensional Taylor-Net Strategy

A resilient solution to Range-Only SLAM based on a decoupled landmark range and bearing reconstruction

A Range Only Simultaneous Localization and Mapping (RO-SLAM) problem is considered in this paper. The robot is a unicycle like vehicle equipped with encoders on the actuated wheels, which measures the distance to a set of UWB landmarks located in unknown position in the surrounding. A Multi Hypotheses Extended Kalman Filter (MHEKF), one for each landmark, is designed to dynamically estimate the range and the bearing of the observed landmark. These estimates, regarded as measurements with a proper covariance matrix, are used in an EKF SLAM algorithm, endowed with a resilient module to discern and possibly to temporarily discard landmarks with an unreliable range and bearing estimate. This allows to cope with the initial uncertainty characterizing the bearing reconstruction, but also to resist the effects of outliers and to detect possible abnormal situations. Simulation and experimental results illustrate the effectiveness of the proposed approach compared to other methods available in the literature, especially in case of significant perturbations, like the sudden and unmodeled shift of the landmarks.

Robust Simultaneous Localization and Mapping Using the Relative Pose Estimation of Trilateration UHF RFID Tags

We consider a mobile robot equipped with wheel encoders and a RFID reader, which measures the phase of the signal backscattered by a set of passive UHF-RFID tags, deployed in unknown position on the ceiling of the environment. We deal with a Simultaneous Localization And Mapping (SLAM) problem, where the position of the tags must be estimated to create a reference map, within which the robot will be localized. One of the contributions of the paper is the use of a special kind of tag, the TriLateration Tag (TLT), including three antennas close one each other. The solution is based on the range and bearing estimation of all TLT detected, performed through a set of Multi-Hypothesis Extended Kalman Filters (MHEKF), one for each TLT. Then, the range and the bearing information of the responding TLTs is used in an EKF-SLAM algorithm which solves the SLAM problem. The proposed approach is more robust and computationally efficient with respect to other approaches available in the literature and is particularly suited for large warehouses where RFID tags cannot be deployed too densely.

A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

PurposeThe purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity.Design/methodology/approachA multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations.FindingsThe simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping.Originality/valueThe method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

Autonomous Navigation Method for Mars Rover Using Distributed EKF-SLAM Assisted by Sun Sensor

Since the existing SLAM algorithm can not meet the actual demand of the Mars rover autonomous navigation problem in real-time and accuracy, a distributed EKF-SLAM algorithm is proposed based on heading assistance to achieve the rover's autonomous navigation. The solar azimuth is got by using the dual axis analog sun sensor, and then the rover heading information is calculated and added it to each subsystem of SLAM. Consequently, a SLAM model of EKF-SLAM system is built and the federal EKF is adopted to realize the state estimation of the distributed SLAM system. Finally, a whole astronomical heading assistant distributed system is constructed. The experiment test is performed in outdoor experimental environment using a mobile robot, and the experimental results demonstrate the accuracy and effectiveness of the proposed algorithm.

An Improved EKF-SLAM for Mars Surface Exploration

In the traditional EKF-SLAM algorithm, the computational complexity and uncertainty will grow up rapidly with the increase of the feature points and the enlargement of the map coverage. As we know, the computational complexity is proportional to the quadratic of the number of feature points contained in a single filtering process. The approach represented in the paper combines EKF-SLAM with local submaps, which can improve the computational efficiency and reduce the computational complexity. At first, an independent local submap is established for the observed feature points. When the number of feature points contained in the local submap reaches a certain threshold value, the local submap is integrated into the global map. At last, the submap is initialized again. The simulation results show that the approach can reduce the computational complexity effectively and increase the computation speed greatly in the case of maintaining the computational accuracy of the traditional EKF-SLAM algorithm.

Cooperative Visual SLAM based on Adaptive Covariance Intersection

Simultaneous localization and mapping (SLAM) is an essential capability for Unmanned Ground Vehicles (UGVs) travelling in unknown environments where globally accurate position data as GPS is not available. It is an important topic in the autonomous mobile robot research. This paper presents an Adaptive De-centralized Cooperative Vision-based SLAM solution for multiple UGVs, using the Adaptive Covariance Intersection (ACI) supported by a stereo vision sensor. In recent years, SLAM problem has gotten a specific consideration, the most commonly used approaches are the EKF-SLAM algorithm and the FAST-SLAM algorithm. The primary, which requires an accurate process and an observation model, suffers from the linearization problem. The last mentioned is not suitable for real-time implementation. In our work, the Visual SLAM (VSLAM) problem could be solved based on the Smooth Variable Structure Filter (SVSF) is proposed. This new filter is robust and stable to modelling uncertainties making it suitable for UGV localization and mapping problem. This new strategy retains the near optimal performance of the SVSF when applied to an uncertain system, it has the added benefit of presenting a considerable improvement in the robustness of the estimation process. All UGVs will add data features sorted by the ACI that estimate position on the global map. This solution gives, as a result, a large reliable map constructed by a group of UGVs plotted on it. This paper presents a Cooperative SVSF-VSLAM algorithm that contributes to solve the Adaptive Cooperative Vision SLAM problem for multiple UGVs. The algorithm was implemented on three mobile robots Pioneer 3-AT, using stereo vision sensors. Simulation results show eciency and give an advantage to our proposed algorithm, compared to the Cooperative EKF-VSLAM algorithm mainly concerning the noise quality. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

LIDAR EKF-SLAM Technology and Its Application in Automated Guided Vehicle Navigation

Automated Guided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF-SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation.

INTEGRATED SYSTEM SIMULTANEUS LOCALIZATION AND MAPPING

In this paper, we consider the solution of the problem of simultaneous localization and the construction of a map for an unmanned aerial vehicle (a quadrocopter). The structure of the integrated navigation system is developed on the basis of the fusion of several sources of navigational information, which allows to compensate the shortcomings of each source, which includes the following blocks: an improved system of visual navigation based on the use of EKF-SLAM, satellite navigation system GPS, barometric altimeter, radio altimeter, Strapdown inertial navigation system, the converter of modes of navigation. To improve the quality of the visual navigation system, an improved EKF-SLAM algorithm is proposed with the adaptation of the surveillance zone and local data association based on the improved ants algorithm, thereby avoiding obstacles. Recognition of landmarks is based on the use of the algorithm SURF. The EKF-SLAM algorithm is integrated through Adaptive Observation Range. Algorithms for dynamically changing the size of the observation zone and determining the redundancy of the detected landmarks are proposed. The extended Kalman filtering procedure for the problem under consideration and the proposed improvements are given. It is shown that the problem of SLAM data association can be represented as an optimization problem. As an optimization algorithm, an ant algorithm is proposed.

Robust SVSF-SLAM for Unmanned Vehicle in Unknown Environment

Abstract: Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. The most popular solutions of this problem are the EKF-SLAM and the FAST-SLAM, the former requires an accurate process and observation model and suffer from the linearization problem, and the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF-SLAM) algorithm is proposed in this paper to solve the Unmanned Ground Vehicle (UGV) SLAM problem. The SVSF filter which is formulated in a predictor-corrector format is robust face parameters uncertainties and error modeling and doesn’t require any assumption about noise characteristics. In this paper the SVSF-SLAM algorithm is implemented using the odometer and LASER data to construct a map of the environment and localize the UGV within this map. The proposed algorithm is validated and compared to the EKF-SLAM algorithm. Good results are obtained by the SVSF-SLAM comparing to the EKF-SLAM especially when the noise is colored or affected by a variable bias. Which confirm the efficiency of our approaches.

EKF-SLAM을 이용한 쿼드콥터의 위치 추정

In this paper, a method for estimating the location of a quadcopter is proposed by applying an EKF-SLAM algorithm to its flight control, to autonomously control the flight of an unmanned quadcopter. The usefulness of this method is validated through simulations. For autonomously flying the unmanned quadcopter, an algorithm is required to estimate its accurate location, and various approaches exist for this. Among them, SLAM, which has seldom been applied to the quadcopter flight control, was applied in this study to simulate a system that estimates flight trajectories of the quadcopter.

Real-Time Monocular SLAM With Low Memory Requirements

The localization of a vehicle in an unknown environment is often solved using simultaneous localization and mapping (SLAM) techniques. Many methods have been developed, each requiring a different amount of landmarks (map size), and thus of memory, to work efficiently. Similarly, the required computational time is quite variable from one approach to another. In this paper, we focus on a monocular SLAM problem and propose a new method called MSLAM, which is based on an extended Kalman filter (EKF). The aim is to provide a solution that has low memory and processing time requirements and that can achieve good localization results while benefiting from EKF advantages (i.e., direct access to the covariance matrix, no conversion required for the measures or the state, etc.). To do so, a minimal Cartesian representation (three parameters for three dimensions) is used. However, linearization errors are likely to happen with such a representation. New methods allowing to avoid or hugely decrease the impact of the linearization failures are presented. The first contribution proposed here computes a proper projection of a 3-D uncertainty in the image plane, allowing to track landmarks during longer periods of time. A corrective factor of the Kalman gain is also introduced. It allows to detect wrong updates and correct them, thus reducing the impact of the linearization on the whole system. Our approach is compared with a classic SLAM implementation over different data sets and conditions to illustrate the efficiency of the proposed contributions. The quality of the map built is tested by using it with another vehicle for localization purposes. Finally, a public data set presenting a long trajectory (1.3 km) is also used in order to compare MSLAM with a state-of-the-art monocular EKF-SLAM algorithm, both in terms of accuracy and computational needs.

The [formula omitted]-learning obstacle avoidance algorithm based on EKF-SLAM for NAO autonomous walking under unknown environments

The two important problems of SLAM and Path planning are often addressed independently. However, both are essential to achieve successfully autonomous navigation. In this paper, we aim to integrate the two attributes for application on a humanoid robot. The SLAM problem is solved with the EKF-SLAM algorithm whereas the path planning problem is tackled via Q-learning. The proposed algorithm is implemented on a NAO equipped with a laser head. In order to differentiate different landmarks at one observation, we applied clustering algorithm on laser sensor data. A Fractional Order PI controller (FOPI) is also designed to minimize the motion deviation inherent in during NAO’s walking behavior. The algorithm is tested in an indoor environment to assess its performance. We suggest that the new design can be reliably used for autonomous walking in an unknown environment.

Real time simultaneous localization and mapping: towards low-cost multiprocessor embedded systems

Simultaneous localization and mapping (SLAM) is widely used by autonomous robots operating in unknown environments. Research community has developed numerous SLAM algorithms in the last 10 years. Several works have presented many algorithms’ optimizations. However, they have not explored a system optimization from the system hardware architecture to the algorithmic development level. New computing technologies (SIMD coprocessors, DSP, multi-cores) can greatly accelerate the system processing but require rethinking the algorithm implementation. This article presents an efficient implementation of the EKF-SLAM algorithm on a multi-processor architecture. The algorithm-architecture adequacy aims to optimize the implementation of the SLAM algorithm on a low-cost and heterogeneous architecture (implementing an ARM processor with SIMD coprocessor and a DSP core). Experiments were conducted with an instrumented platform. Results aim to demonstrate that an optimized implementation of the algorithm, resulting from an optimization methodology, can help to design embedded systems implementing low-cost multiprocessor architecture operating under real-time constraints.