An Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter (original) (raw)
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Mobile Robot Localization via Unscented Kalman Filter
2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2019
Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.
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.
International Journal of Electrical and Computer Engineering (IJECE), 2020
In this paper we introduce a cubic root unscented kalman filter (CRUKF) compared to the unscented kalman filter (UKF) for calculating the covariance cubic matrix and covariance matrix within a sensor fusion algorithm to estimate the measurements of an omnidirectional mobile robot trajectory. We study the fusion of the data obtained by the position and orientation with a good precision to localize the robot in an external medium; we apply the techniques of kalman filter (KF) to the estimation of the trajectory. We suppose a movement of mobile robot on a plan in two dimensions. The sensor approach is based on the CRUKF and too on the standard UKF which are modified to handle measurements from the position and orientation. A real-time implementation is done on a three-wheeled omnidirectional mobile robot, using a dynamic model with trajectories. The algorithm is analyzed and validated with simulations. 1. INTRODUCTION The mobile robots are equipped with sensors to measure the distance of the robot from the space of the objects where they move. Measurements which are always linked by noises are then fused together in a filter to obtain an estimate of the position and orientation of the mobile robot trajectory in the space and plan [1, 2]. The most important problems related to robots are situated in sensors powere by their battery which reduces the autonomy. Also there are situations where sensors cannot function simultaneously, when they use the same frequency band [3, 4]. The Kalman filter is an optimal linear estimator when the process noise and the measurement noise can be modeled by white Gaussian noise. Non-linear problems can be solved with the unscented Kalman filter (UKF) and we propose a new algorithm that is called the cubic root unscented Kalman filter (CRUKF) to solve also nonlinear problems. In our work, we use this technique to estimate the position and the angle of the robot in its displacement then a comparison between the performances of the two filters is presented to give an evaluation of more precised measurement and will appreciate better statistical properties [2, 5]. This paper is organized as follow; we present the modeling of the mobile robot with the equations of trajectory and angles of the robot. Next, we pass to the presentation of the UKF algorithm and we propose a CRUKF algorithm to improve the accuracy of the state estimate. Finally we present and discuss the simulation results and outline some possible extensions for future investigations.
On the Kalman Filter Approach for Localization of Mobile Robots
2016
In this work we analyze robot motion given from the UTIAS Multi-Robot Dataset. The dataset contains recordings of robots wandering in a confined environment with randomly spaced static landmarks. After some preprocessing of the data, an algorithm based on the Extended Kalman Filter is developed to determine the positions of robots at every instant of time using the positions of the landmarks. The algorithm takes into account the asynchronous time steps and the sparse measurement data to develop its estimates. These estimates are then compared with the groundtruth data provided in the same dataset. Furthermore several methods of noise estimation are tested, which improve the error of the estimate for some robots.
2011
Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckon...
A novel fuzzy Kalman filter for mobile robots localization
A new method to implement fuzzy Kalman filters is introduced in this paper. This has special application in fields where inaccurate models or sensors are involved, such as in mobile robotics. The innovation consists in using possibility distributions, instead of gaussian distributions. The main advantage of this approach is that uncertainty is not needed to be symmetric, while a region of possible solutions is allowed. The contribution of this work also includes a method to propagate uncertainty through both the process and the observation models. This one is based on quantifying uncertainty as trapezoidal possibility distributions. Finally, the way to reduce the EKF inconsistence when large number of iterations are carried out is shown.
Application of Kalman Filter to estimate position of a mobile node in Indoor environments
2016
Estimating the position of mobile agents in indoor environments is a challenging problem especially when the estimates must be obtained using commercial, low cost devices. The work in this thesis presents experimental results that demonstrate the effectiveness of our approach, that integrates the signal strength data with sensed values of acceleration and angular velocity. A well-known framework called the Kalman Filter is used. To cope with the noise in the measured values, different versions of the Kalman Filter had to be used such as the Extended Kalman Filter and the Unscented Kalman Filter. This framework allowed us to systematically fuse the data from multiple sources to improve the accuracy of the position estimates. Our results demonstrate that positional accuracy of 0.8m within an 30m x 10m area is achieved. In the future, this work can be extended to further reduce the error in the location estimates by inclusion of encoders.
Mobile Robot Localization Based on Kalman Filter
2000
Robots facilitate exploration of hazardous environments during response to catastrophe. Autonomous robotic platforms involved in search and rescue operations require accurate position and orientation (localization) information for self-navigation from its current position to its subsequent destination. A Hybrid Routing Algorithm Model has been proposed by the SPACE (structures, pointing and control engineering) URC (university research center) at California State University of Los Angeles. This model envisions three-layered terrain mapping with obstacle representations from various information sources such as satellites, UAVs and onboard range sensors. A* path-finding algorithm is applied to the outer two layers of the model (Layer 1 and Layer 2), while dynamic A* algorithm is responsible for innermost layer (Layer 3) navigation. The mobile robot localization information is computed using data obtained from a 9 Degrees of Freedom Inertial Measurement Unit. While gyroscope sensors provide the system the instantaneous radial velocity of a turning platform, these sensors are also susceptible to drift. Accelerometers are extremely sensitive to vibrations, and along with fluctuating magnetic fields, both accelerometers and magnetometers exhibit noisy behaviors when localizing the robot. Since the IMU contains all three sensors, a Kalman Filter is implemented on a PSoC-5 microcontroller to fuse data from the IMU sensors. This reduces standard deviation between measurements and improves reported heading accuracy, hence provides reliable information on the robot's localization and improves mapping.
Divided Difference Kalman Filter for indoor mobile localization
International Conference on Indoor Positioning and Indoor Navigation, 2013
Knowledge of the positions of sensor nodes is crucial for numerous applications in wireless sensors network. In this paper, we propose to use the Divided Difference Kalman Filter (DDKF) as a solution for locating and tracking a mobile node. This approach is an alternative variant of the nonlinear Kalman filtering already used in this type of applications. The advantage of this approach is that it does not require calculation of the Jacobian as for the Extended Kalman Filter (EKF) and it does not need to use several parameters, as for the Unscented Kalman Filter (UKF) which accuracy is closely dependent on the good choice of such parameters. In this work, a comparative performance study of four localization methods was conducted, namely the DDKF, the EKF, the UKF and the Least Squares Kalman Filter (LS-KF) which is a method based on multilateration in the least squares sense, followed by a smoothing step, using Kalman filtering. This study reveals many advantages in favor of the DDKF which, when applied for indoor localization, provides up to 40% gain in terms of Root Mean Squares Errors (RMSE) in position estimation, as compared to the other considered methods and which has a location error that is less than 2 meters in 95% of the considered cases.
1999
The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transfered to another mobile platform provided it carries an equivalent set of sensors. The 2 0 case is studied in detail first. Results of extending the approach to the 3 0 case are presented. In both cuses the results demonstrate the eficacy of the proposed method. 0-7803-51 80-0-5199 $10.00 0 1999 IEEE