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Map Building and Localization Based on Wave Algorithm and Kalman Filter
Saitov, Dilshat,Choi, Jeong Won,Park, Ju Hyun,Lee, Suk Gyu Institute of Embedded Engineering of Korea 2008 대한임베디드공학회논문지 Vol.3 No.2
This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.
Effective Map Building Using a Wave Algorithm in a Multi-Robot System
Saitov, Dilshat,Umirov, Ulugbek,Park, Jung-Il,Choi, Jung-Won,Lee, Suk-Gyu Korean Society for Precision Engineering 2008 International Journal of Precision Engineering and Vol.9 No.2
Robotics and artificial intelligence are components of IT that involve networks, electrical and electronic engineering, and wireless communication. We consider an algorithm for efficient navigation by building a precise map in a multi-robot system under conditions of limited and unlimited communications. The basis of the navigation algorithm described in this paper is a wave algorithm, which is effective in obtaining an accurate map. Each robot in a multi-robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robots can actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize exploration efficiency. To prove the efficiency of the proposed technique, we compared the final results with the results in $Burgard^{8}$ and $Stachniss.^{9-10}$ All of the simulation comparisons, which are shown as graphs, were made in four different environments.
Localization and Map Building using SLAM and Wave Algorithm
Dilshat Saitov,Suk Gyu Lee,Jeh Won Lee 대한전자공학회 2008 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
This paper mainly concentrates on an algorithm for efficient navigation by building a precise map in a multirobot system under conditions of limited and unlimited communications. The navigation algorithm described in this paper is a wave algorithm, which is effective in obtaining an accurate map. Each robot in a multirobot system has its own task such as building a map for its local position. By combining their data into a shared map, the robots can actively seek to verify their relative locations. The localization algorithm was extracted from the algorithm, which is well known as SLAM (Simultaneous Localization and Mapping)
Effective Map Building Using a Wave Algorithm in a Multi-Robot System
Dilshat Saitov,Ulugbek Umirov,Jung Il Park,Jung Won Choi,Suk Gyu Lee 한국정밀공학회 2008 International Journal of Precision Engineering and Vol.9 No.2
Robotics and artificial intelligence are components of IT that involve networks, electrical and electronic engineering, and wireless communication. We consider an algorithm for efficient navigation by building a precise map in a multi-robot system under conditions of limited and unlimited communications. The basis of the navigation algorithm described in this paper is a wave algorithm, which is effective in obtaining an accurate map. Each robot in a multi-robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robots can actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize exploration efficiency. To prove the efficiency of the proposed technique, we compared the final results with the results in Burgard? and Stachniss.??¹? All of the simulation comparisons, which are shown as graphs, were made in four different environments.
Dilshat Saitov,이석규 한국정밀공학회 2011 International Journal of Precision Engineering and Vol.12 No.3
This paper mainly focuses on the improvement of the navigation in mobile robot systems. There are three key components of a mobile robot system such as the ability to localize itself accurately and, simultaneously, to build a map of the surroundings, and finally to navigate effectively in an unknown environment. This paper mainly focused on the establishment of a methodology to model the control system that optimizes the behavior rules using EWA (Extended Wave Algorithm). Heuristic method of the EWA lies in finding the most prospective destination point, while navigation based on WA (Wave Algorithm) always uses the closest frontier cell as the next destination point. Also, this paper takes into account map merging and particle filter as map building and localization techniques, respectively. The overall algorithm has been tested extensively in simulation runs. The results given in this paper demonstrate that our algorithm significantly reduces the exploration time compared to previous approaches.
Map building and Localization based on Wave Algorithm and Kalman Filter
Dilshat Saitov,Jeong Won Choi,Ju Hyun Park,Suk Gyu Lee 대한임베디드공학회 2008 대한임베디드공학회논문지 Vol.3 No.2
This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.
이동 로봇을 위한 하이브리드 이미지 안정화 시스템의 개발
최윤원(Yun Won Choi),강태훈(Tae Hun Kang),Dilshat Saitov,이동춘(Dong Chun Lee),이석규(Suk Gyu Lee) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.2
This paper proposes a hybrid image stabilizing system which uses both optical image stabilizing system based on EKF (Extended Kalman Filter) and digital image stabilization based on SURF (Speeded Up Robust Feature). Though image information is one of the most efficient data for object recognition, it is susceptible to noise which results from internal vibration as well as external factors. The blurred image obtained by the camera mounted on a robot makes it difficult for the robot to recognize its environment. The proposed system estimates shaking angle through EKF based on the information from inclinometer and gyro sensor to stabilize the image. In addition, extracting the feature points around rotation axis using SURF which is robust to change in scale or rotation enhances processing speed by removing unnecessary operations using Hessian matrix. The experimental results using the proposed hybrid system shows its effectiveness in extended frequency range.