IRJET- Simulation of Localization for Indoor Mobile Robot using Extended Kalman Filter (original) (raw)
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Enhancement of mobile robot localization using extended Kalman filter
Advances in Mechanical Engineering, 2016
In this article, we introduce a localization system to reduce the accumulation of errors existing in the dead-reckoning method of mobile robot localization. Dead-reckoning depends on the information that comes from the encoders. Many factors, such as wheel slippage, surface roughness, and mechanical tolerances, affect the accuracy of dead-reckoning. Therefore, an accumulation of errors exists in the dead-reckoning method. In this article, we propose a new localization system to enhance the localization operation of the mobile robots. The proposed localization system uses the extended Kalman filter combined with infrared sensors in order to solve the problems of dead-reckoning. The proposed system executes the extended Kalman filter cycle, using the walls in the working environment as references (landmarks), to correct errors in the robot's position (positional uncertainty). The accuracy and robustness of the proposed method are evaluated in the experiment results' section.
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.
2013 16th International Conference on Advanced Robotics (ICAR), 2013
The present paper faces the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A set of segments is used to model the robot surrounding environment and segments' starting and ending points are used as SLAM landmarks. A segment based mapping algorithm is proposed and used along with an Extended Kalman filter driven by measurements taken by ultrasonic sensors located on the robot. The proposed SLAM algorithm has been tested in both simulated and real experiments yielding to encouraging estimation and mapping results.
IJERT-Vision and Simultaneous Localization and Mapping based Autonomous Indoor Mobile Robot
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/vision-and-simultaneous-localization-and-mapping-based-autonomous-indoor-mobile-robot https://www.ijert.org/research/vision-and-simultaneous-localization-and-mapping-based-autonomous-indoor-mobile-robot-IJERTV10IS050354.pdf The Simultaneous Localization and Mapping (SLAM) has been one of the most studied subjects in Robotics. It is a fundamental process in mobile robotics, since it is responsible for building maps and at same time estimate the robot position in the environment. In this paper, a real-time implementation of simultaneous localization and mapping (SLAM) is described based on a mobile robot platform, which consists of two driving wheels, one caster wheel, an ultrasonic sensor and a Kinect sensor camera. The robot used for this purpose is differential drive robot. The main algorithm used is extended Kalman Filter (EKF) which is combined with Visual SLAM to handle data association problems and to map the trajectory with more accuracy. A mathematical equations of state space model and observation model is used to represents the motion of a robotic system from one timestep to the next and to infer what the sensor measurement would be at that timestep, respectively. The robot's built-in ability allows it to map the interiors of unknown environment and run autonomously using its self ruling and pilot feature. Robot Operating System is used for data processing and analysis of mobile robot platform. It is intended to implement the proposed SLAM by Extended Kalman Filter and VSLAM using low cost, energy efficient Raspberry PI as embedded system, IMU, laptop and ROS to verify the utility of proposed methods as a indoor wireless mobile robot.
Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM
IAES International Journal of Robotics and Automation (IJRA), 2021
Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX-UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF-SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.
Journal of Automation, Mobile Robotics & Intelligent Systems, 2014
Localiza on in an unknown environment is one of the major issues faced by autonomous vehicles. The solu on to this problem is delivered by the Simultaneous Localiza on and Mapping techniques, commonly known as SLAM. SLAM is the category of algorithms allowing a robot to map the surroundings and to keep an es mate of its posi on. Nowadays several SLAM methods are widely used. Though, many issues arise when SLAM is applied in a complex and unstructured environment. This ar cle details an implementa on of SLAM using improved Extended Kalman Filter (EKF). The aim is to provide a simple but reliable SLAM technique. The work has been carried out on a robot Seekur Jr, the mapping has been realized with a laser scanner. The applied EKF model with its modifica ons is presented. The techniques used to observe the environment and to iden fy the landmarks are outlined. The robustness and consistency of introduced modificaons were jus fied by experiments.
A new Extended Kalman Filter based on actual local information for mobile robots
9th European Workshop on Advanced Control and Diagnosis (ACD 2011), 2011
In this work we propose a new version of Extended Kalman Filter for robot localization. Such algorithm is based on local data and does not require any assumption on robot's working environment. The filter is driven by measurements, taken by ultrasonic sensors located onboard the robot, and a switching sensors activation policy is devised, which allows power saving still achieving accurate tracking. We obtain reliable estimation results, as shown in simulations. The proposed algorithm works well both in "simple" and in "complex" environments.
Localization of a Mobile Robot based in Odometry and Natural Landmarks using Extended Kalman Filter
2008
This work proposes a localization system for mobile robots using the Extended Kalman Filter. The robot navigates in an known environment where the lines of the floor are used as natural landmarks and identifiqued by using the Hough transform.The prediction phase of the Kalman Filter is implemented using the odometry model of the robot. The update phase directly uses the parameters of the lines detected by the Hough algorithm to correct the robot's pose.
Revista de Investigación, Desarrollo e Innovación
Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks.