Kalman Filtering-Aided Optical Localization of Mobile Robots: System Design and Experimental Validation

Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications ◽

10.1115/dscc2017-5368 ◽

2017 ◽

Cited By ~ 2

Author(s):

Jason N. Greenberg ◽

Xiaobo Tan

Keyword(s):

Kalman Filter ◽

Mobile Robots ◽

Kalman Filtering ◽

Experimental Validation ◽

Line Of Sight ◽

Localization Error ◽

Two Dimensional ◽

Prior Work ◽

Localization Performance ◽

Mobile Base

Localization of mobile robots in GPS-denied envrionments (e.g., underwater) is of great importance to achieving navigation and other missions for these robots. In our prior work a concept of Simultaneous Localization And Communication (SLAC) was proposed, where the line of sight (LOS) requirement in LED-based communication is exploited to extract the relative bearing of the two communicating parties for localization purposes. The concept further involves the use of Kalman filtering for prediction of the mobile robot’s position, to reduce the overhead in establishing LOS. In this work the design of such a SLAC system is presented and experimentally evaluated in a two-dimensional setting, where a mobile robot localizes itself through wireless LED links with two stationary base nodes. Experimental results are presented to demonstrate the feasibility of the proposed approach and the important role the Kalman filter plays in reducing the localization error. The effect of the distance between the base nodes on the localization performance is further studied, which bears implications in future SLAC systems where mobile base nodes can be reconfigured adaptively to maximize the localization performance.

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Efficient Optical Localization for Mobile Robots via Kalman Filtering-Based Location Prediction

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control ◽

10.1115/dscc2016-9917 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jason N. Greenberg ◽

Xiaobo Tan

Keyword(s):

Kalman Filter ◽

Kalman Filtering ◽

Mobile Agent ◽

Optical Communication ◽

Line Of Sight ◽

Location Prediction ◽

Two Dimensional ◽

Optical Communication System ◽

Search Range ◽

Simulation Results

Localization and communication are both essential functionalities of any practical mobile sensor network. Achieving both capabilities through a single Simultaneous Localization And Communication (SLAC) would greatly reduce the complexity of system implementation. In this paper a technique for localizing a mobile agent using the line of sight (LOS) detection of an LED-based optical communication system is proposed. Specifically, in a two-dimensional (2D) setting, the lines of sight between a mobile robot and two base nodes enable the latter to acquire bearing information of the robot and compute its location. However, due to the mobile nature of the robot, establishing its LOS with the base nodes would require extensive scan for all parties, severely limiting the temporal resolution and spatial precision of the localization. We propose the use of a Kalman filter to predict the position of the robot based on past localization results, which allows the nodes to significantly reduce the search range in establishing LOS. Simulation results and preliminary experimental results are presented to illustrate and support the proposed approach.

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Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots

IEEE Transactions on Robotics and Automation ◽

10.1109/70.760343 ◽

1999 ◽

Vol 15 (2) ◽

pp. 219-229 ◽

Cited By ~ 202

Author(s):

L. Jetto ◽

S. Longhi ◽

G. Venturini

Keyword(s):

Kalman Filter ◽

Mobile Robots ◽

Extended Kalman Filter ◽

Experimental Validation ◽

Adaptive Extended Kalman Filter

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Improved GNSS Localization with the Use of DOP Parameter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.611.450 ◽

2014 ◽

Vol 611 ◽

pp. 450-466 ◽

Cited By ~ 1

Author(s):

František Duchoň ◽

Jaroslav Hanzel ◽

Andrej Babinec ◽

Jozef Rodina ◽

Peter Paszto ◽

...

Keyword(s):

Kalman Filter ◽

Mobile Robots ◽

Reference Frame ◽

Small Scale ◽

Localization Error ◽

Localization Accuracy ◽

Gnss Receiver ◽

Gps Localization

This paper presents the approach to improve localization based on GNSS. The principles of the GPS localization and impact of the DOP parameter on localization error are mathematically analyzed. The algorithm based on the use of DOP parameter and Kalman filter for the improvement of the localization accuracy suitable for small scale outdoor mobile robots and other outdoor applications is proposed. The applicability of the proposed methodology was verified by performed experiments with two common cheap miniature GPS modules and accurate high-end GNSS receiver used as a reference frame for the measurements. The obtained results affirmed the improvement of the localization accuracy.

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Mutual Localization of Multiple Sensor Node Robots

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p1269 ◽

2011 ◽

Vol 15 (9) ◽

pp. 1269-1276 ◽

Cited By ~ 1

Author(s):

Keitaro Naruse ◽

◽

Shigekazu Fukui ◽

Jie Luo

Keyword(s):

Kalman Filter ◽

Mobile Robots ◽

Extended Kalman Filter ◽

Sensor Node ◽

Localization Error ◽

Localization Problem ◽

Position Information ◽

Omnidirectional Camera ◽

Amount Of Information ◽

Multiple Sensor

The objective of this paper is to develop a localization systemof cooperativemultiple mobile robots, in which each robot is assumed to observe a set of known landmarks and equipped with an omnidirectional camera. In this paper, it is assumed that a robot can detect other robots by using the omnidirectional camera, share its estimated position with others, and utilize shared positions for its localization. In other words, each robot can be viewed as an additional mobile landmark to a set of stationary landmarks. A foremost concern is how well this system performs localization under a limited amount of information. This paper presents an investigation of self localization error of each robot in a group using Extended Kalman Filter to solve the localization problem with the insufficient landmarks and inaccurate position information.

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Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410018813053 ◽

2018 ◽

Vol 233 (11) ◽

pp. 4017-4031

Author(s):

Kai Xiong ◽

Chunling Wei ◽

Haoyu Zhang

Keyword(s):

Kalman Filter ◽

Kalman Filtering ◽

Autonomous Navigation ◽

Adaptive Estimation ◽

Line Of Sight ◽

Parallel Model ◽

Multiple Model ◽

Filtering Algorithm ◽

Robust Kalman Filter ◽

Multiple Model Adaptive Estimation

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

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CALCULATION OF THE DEPENDENCE OF THE DISTANCE TO THE LOCATED OBJECT FROM THE CORNER POSITION OF THE VEHICLE LINE

Issues of radio electronics ◽

10.21778/2218-5453-2018-4-14-18 ◽

2018 ◽

pp. 14-18

Author(s):

V. V. Artyushenko ◽

A. V. Nikulin

Keyword(s):

Real Time ◽

Statistical Physics ◽

Angular Position ◽

Three Dimensional ◽

Line Of Sight ◽

Two Dimensional ◽

Radar Station ◽

Flight Mode ◽

Vector Algebra ◽

Analytical Expressions

To simulate echoes from the earth’s surface in the low flight mode, it is necessary to reproduce reliably the delayed reflected sounding signal of the radar in real time. For this, it is necessary to be able to calculate accurately and quickly the dependence of the distance to the object being measured from the angular position of the line of sight of the radar station. Obviously, the simplest expressions for calculating the range can be obtained for a segment or a plane. In the text of the article, analytical expressions for the calculation of range for two-dimensional and three-dimensional cases are obtained. Methods of statistical physics, vector algebra, and the theory of the radar of extended objects were used. Since the calculation of the dependence of the range of the object to the target from the angular position of the line of sight is carried out on the analytical expressions found in the paper, the result obtained is accurate, and due to the relative simplicity of the expressions obtained, the calculation does not require much time.

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