Mobile Robot Localization Based on Kalman Filter (original) (raw)
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Implementation of Kalman Filter on PSoC-5 Microcontroller for Mobile Robot Localization
2014
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 pr...
Adaptive Kalman Filtering for GPS-based Mobile Robot Localization
2007 IEEE International Workshop on Safety, Security and Rescue Robotics, 2007
Kalman filters have been widely used for navigation in mobile robotics. One of the key problems associated with Kalman filter is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS-based localization of a rough-terrain mobile robot, the maneuver of the vehicle and the level of measurement noise are environmental dependent, and hard to be predicted. This is particularly true when the vehicle experiences a sudden change of its state, which is typical on rugged terrain due, for example, to an obstacle or slippery slopes. Therefore to assign constant noise levels for such applications is not realistic. In this work we propose a real-time adaptive algorithm for GPS data processing based on the observation of residuals. Large value of residuals suggests poor performance of the filter that can be improved giving more weight to the measurements provided by the GPS using a fading memory factor. For a finer gradation of this parameter, we used a fuzzy logic inference system implementing our physical understanding of the phenomenon. The proposed approach was validated in experimental trials comparing the performance of the adaptive algorithm with a conventional Kalman filter for vehicle localization. The results demonstrate that the novel adaptive algorithm is much robust to the sudden changes of vehicle motion and measurement errors.
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
Developing a real time navigation for the mobile robots at unknown environments
Indonesian Journal of Electrical Engineering and Computer Science, 2020
Mobile robot needs to navigate at unknown environments and constructing its maps at the same time. Therefore, we proposed to use an algorithm named simultaneous localization and mapping (SLAM). Then, we suggested the extended kalman filter algorithm (EKF) to solve the SLAM problem which is implemented at different unknown environments containing a different number of landmarks where the detectable landmarks will play an important role in controlling the overall navigation process and on EKF-SLAM technique's performance. MATLAB simulation results show that the performance of EKF-SLAM path is enhanced as the number of landmarks increased, so the performance becomes better as compared with an odometry path depending on the value of mean square error. After that, we simulated mobile robot platform named TurtleBot2e in Gazebo simulator to achieve the SLAM algorithm for different environments based on G-mapping algorithm which was built on robot operating system (ROS). The main contribution that comes with this work is the simulation of SLAM technique is done by using two different software platforms separately (MATLAB and ROS). Finally, the execution time to build a map is computed for each environment in Gazebo simulator, and we concluded that it is increased when the landmarks are increased.
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.
Introduction to Kalman Filter and Its Applications
Introduction and Implementations of the Kalman Filter [Working Title]
We provide a tutorial-like description of Kalman filter and extended Kalman filter. This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. In conclusion, this chapter will become a prerequisite for other contents in the book.
Design and implementation of a navigation system for autonomous mobile robots
International Journal of Ad Hoc and Ubiquitous Computing, 2010
The robotic navigation system is one of the most important and fundamental components of the successful robots. A navigation system of a robot is the key to the excellent motion of the robot. In this thesis, a navigation system for autonomous mobile robot is proposed. Our navigation system is a hybrid of behavior-based and model-based navigation systems. In our system, behavior-based subsystem is in charge of low-level reactive actions, and model-based subsystem is responsible for high-level planned actions. Besides, our system can communicate with wireless sensor network and utilize the localization technology of wireless sensor network to calibrate the estimated position of the robot. When the robot is going to leave for a destination, our system will utilize model-based subsystem to compute a path from the robot to the destination. Then, it divides this path into many virtual points, and the behavior-based subsystem is going to approach each virtual point in turn. If there are some obstacles in the way, the navigation system will use our obstacle avoidance algorithm to avoid these obstacles and keep the robot toward the destination. Therefore, our robot will arrive at the destination correctly. Furthermore, we use multi-thread technology to establish our navigation system. Thus, our system can run important modules concurrently and can utilize the multi-core processor more efficiently. Based on our experimental results, our navigation system can navigate the robot in the passages with obstacles effectively and would be applied extensively.
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.
IEEE Transactions on Robotics and Automation, 1999
A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an on line adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.
Mobile Robot Controlling Possibilities of Inertial Navigation System
Procedia Engineering, 2016
The paper explain analysis of inertial navigation system and accelerometric, gyroscopic sensors and describe possibilities of their application for inertial navigation of mobile robot. Such controlling system allows to monitor exact position of robot. These information can be applied for robot controlling, its autonomous control or its tracking. Inertial navigation is completely autonomous and independent from surroundings, i.e. the system is resistant from external influences as magnetic disturbances, electronically disturbance, signal deformation, etc. For mobile robots to be successful, they have to move safely in environments populated and dynamic. While recent research has led to a variety of localization methods that can track robots well in static environments, we still lack methods that can robustly localize mobile robots in dynamic environments.