• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.83-85
• /
• 2005

Exploration involving mapping and localization in an unknown environment is an important task in mobile robots. For this, robot must be able to build a reliable map of surroundings and to estimate the position of it. In this paper, we developed technique for gird-based localization of a mobile robot with ultrasonic sensors using EKF(Extended Kalman Filter). We also describe the information about landmarks detected in the environment. Finally, the robot experiments show the efficiency of our approach in the real environment.

• The Journal of Korea Robotics Society
• /
• v.11 no.1
• /
• pp.9-18
• /
• 2016

A rectangle-based relative localization method is proposed for a mobile robot based on a novel geometric formulation. In an artificial environment where a mobile robot navigates, rectangular shapes are ubiquitous. When a scene rectangle is captured using a camera attached to a mobile robot, localization can be performed and described in the relative coordinates of the scene rectangle. Especially, our method works with a single image for a scene rectangle whose aspect ratio is not known. Moreover, a camera calibration is unnecessary with an assumption of the pinhole camera model. The proposed method is largely based on the theory of coupled line cameras (CLC), which provides a basis for efficient computation with analytic solutions and intuitive geometric interpretation. We introduce the fundamentals of CLC and describe the proposed method with some experimental results in simulation environment.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.554-556
• /
• 2006

A key component for autonomous mobile robot is to localize ifself. In this paper we suggest a vision-based localization and compensation of robot's location using ultrasound. Mobile robot travels along wall and searches each feature in indoor environment and transformed absolute coordinates of actuality environment using these points and builds a map. And we obtain information of the environment because mobile robot travels along wall. Localzation search robot's location candidate point by ultrasound and decide position among candidate point by features matching.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.9
• /
• pp.903-909
• /
• 2007

Mobile Robots are increasingly being used to perform tasks in unknown environment. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. To achieve autonomous mobile robot navigation, efficient path planner and accurate localization technique are the fundamental issues that should be addressed. This paper presents mobile robot localization using IRID(InfraRed IDentification) as artificial landmarks. IRID has highly deterministic characteristics, different from RFID. By putting several IRID emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Dead-reckoning provides the estimated robot configuration but the error becomes accumulated as the robot travels. IRID information tells the sector the robot is in, but the size of the uncertainty is too large if only the IRID information is used. This paper presents an algorithm which combines both the encoder and the IRID information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed IRID and the proposed algorithm are verified from the simulation results and experiments.

• The Journal of Korea Robotics Society
• /
• v.2 no.3
• /
• pp.255-261
• /
• 2007

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.9
• /
• pp.916-921
• /
• 2011

Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.434-443
• /
• 2007

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.4
• /
• pp.280-285
• /
• 2011

Recently, with the development of service robots and the concept of ubiquitous, the position estimation of mobile objects has received great interest. Some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can acquire the ultrasonic signals from only one or two beacons, due to the obstacles located along the moving path. In this paper, a position estimation scheme using fewer than three sensors is developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.3
• /
• pp.206-210
• /
• 2011

Vision-based robot localization is challenging due to the vast amount of visual information available, requiring extensive storage and processing time. To deal with these challenges, we propose the use of features extracted from omni-directional panoramic images and present a method for localization of a mobile robot equipped with an omni-directional camera. The core of the proposed scheme may be summarized as follows : First, we utilize an omni-directional camera which can capture instantaneous $360^{\circ}$ panoramic images around a robot. Second, Nodes around the robot are extracted by the correlation coefficients of Circular Horizontal Line between the landmark and the current captured image. Third, the robot position is determined from the locations by the proposed correlation-based landmark image matching. To accelerate computations, we have assigned the node candidates using color information and the correlation values are calculated based on Fast Fourier Transforms. Experiments show that the proposed method is effective in global localization of mobile robots and robust to lighting variations.

• The Journal of Korea Robotics Society
• /
• v.14 no.4
• /
• pp.293-300
• /
• 2019

In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.