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Feature extracted algorithm for simultaneous localization and mapping (SLAM)
2015 IEEE International Conference on Consumer Electronics (ICCE), 2015The problem of SLAM is still a challenging issue. When the number of landmarks increases, the accuracy of the estimated location of the robot decreases. Therefore, current measurement is filtered to avoid wrong landmarks. Then, triangulation is used to update the robot's pose. Simulation results show the success of the proposed algorithm.
Yan-Jhang Shih +3 more
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Sliding Mode SLAM for Robust Simultaneous Localization and Mapping
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018Normal SLAMs use the extended Kalman filter to estimate robot localization and the mapping simultaneously. They do not work well under big disturbances and bounded noises. In this paper, the sliding mode method is applied for the SLAM. The proposed sliding model SLAM only requires the noises and the disturbances are bounded.
Salvador Ortiz 0001 +2 more
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A solution to the simultaneous localization and map building (SLAM) problem
IEEE Transactions on Robotics and Automation, 2001The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location.
Gamini Dissanayake +4 more
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Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012Simultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots.
Tuna G. +3 more
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Topological Gaussian ARAM for Simultaneous Localization and Mapping (SLAM)
2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS), 2012This paper proposes a new neural architecture called Topological Gaussian ARAM (TGARAM) for Simultaneous Localization and Mapping (SLAM). TGARAM is integrating the Gaussian classifier with the incremental topology-learning mechanisms of the Growing Neural Gas (GNG) model for online learning of multidimensional inputs and topological map building.
Wei Hong Chin, Chu Kiong Loo
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Analysis of positioning uncertainty in simultaneous localization and mapping (SLAM)
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2005This paper studies the time evolution of the covariance of the position estimates in single-robot simultaneous localization and mapping (SLAM). A closed-form expression is derived, that establishes a functional relation between the noise parameters of the robot's proprioceptive and exteroceptive sensors, the number of features being mapped, and the ...
Anastasios I. Mourikis +1 more
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Filter design for simultaneous localization and map building (SLAM)
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2003This paper deals with the fusion of random variables when cross covariances are unknown. This is a vital problem in nearly every real world application since cross covariances are often impossible to obtain, but also cannot be ignored. We provide a rigorous derivation of the fusion equations which are also known as covariance intersection.
Christian Schlegel, Thomas Kämpke
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ROSLAM—A Faster Algorithm for Simultaneous Localization and Mapping (SLAM)
2016Computationally efficient SLAM (CESLAM) has been proposed to solve simultaneous localization and mapping problem in real-time design. CESLAM first uses the landmark measurement with the maximum likelihood to update the particle states and then update their associated landmarks later.
Teng-Wei Huang +3 more
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An approach to appearance-based simultaneous localization and map building (SLAM)
IEEE Conference on Robotics, Automation and Mechatronics, 2004., 2005Current robotic localization and SLAM algorithms are restricted to simple geometric features such as lines and corners as landmarks. The richness of the information provided by visual perception has not been fully explored. This paper presents a vision based SLAM algorithm which utilizes visual information with minimal prior assumptions.
Chun-Fan Lee, Arcot Sowmya
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An evolutionary algorithm for simultaneous localization and mapping (SLAM) of mobile robots
Advanced Robotics, 2007This paper presents a novel algorithm for simultaneous localization and mapping (SLAM) of mobile robots. The algorithm, termed Evolutionary SLAM, is based on an island model genetic algorithm (IGA). The IGA searches for the most probable map(s) such that the underlying robot's pose(s) provide(s) a robot with the best localization information.
Momotaz Begum +2 more
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