Results 231 to 240 of about 24,069 (274)

Combinatorial maps for simultaneous localization and map building (SLAM)

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2005
In this article, we focus on environment models for the well-known simultaneous localisation and map building (SLAM) problem, which has received considerable attention in the robotics community over the past few years. First, we compare different existing map representations to discuss their advantages and limitations in the scope of indoor robotics ...
D. Dufourd, R. Chatila, D. Luzeaux
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Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping

ACM Transactions on Embedded Computing Systems, 2022
Localization in urban environments is becoming increasingly important and used in tools such as ARCore [18], ARKit [34] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and
Ali J. Ben Ali, Marziye Kouroshli, Sofiya Semenova, Zakieh Sadat Hashemifar, Steven Y. Ko, Karthik Dantu   +5 more
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Simultaneous localization and mapping (SLAM)

2009
In chapter 4 the localization problem is introduced, which is the estimation of the position and orientation of the amr in its environment. It is shown that this problem can be solved with specific sensors or based on specific features of the environment.
Karsten Berns, Ewald von Puttkamer
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Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012
Simultaneous 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., Gulez K., Cagri Gungor V., Veli Mumcu T.   +3 more
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Feature extracted algorithm for simultaneous localization and mapping (SLAM)

2015 IEEE International Conference on Consumer Electronics (ICCE), 2015
The 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, Chen-Chien Hsu, Wei-Yen Wang, Yin-Tien Wang   +3 more
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Computationally efficient algorithm for simultaneous localization and mapping (SLAM)

2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), 2013
FastSLAM is a popular method to solve the problem of simultaneous localization and mapping. However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in particles.
null Cheng-Kai Yang, null Chen-Chien Hsu, null Yin-Tien Wang   +2 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, 2018
Normal 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, Wen Yu, Erik Zamora
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Simultaneous Localization and Mapping (SLAM) in Mobile Robots

2013
This chapter first introduces the concept of SLAM for navigation of mobile robots and then describes the extended Kalman filter (EKF) based SLAM algorithms in detail. Next we consider a more complex scenario where this EKF based SLAM algorithm is implemented in presence of incorrect knowledge of sensor statistics and discuss how fuzzy or neuro-fuzzy ...
Amitava Chatterjee, Anjan Rakshit, N. Nirmal Singh   +2 more
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Topological Gaussian ARAM for Simultaneous Localization and Mapping (SLAM)

2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS), 2012
This 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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Simultaneous Localization and Mapping (SLAM) for warehouse applications

International Journal of Science and Research Archive
The ravaging impacts of the COVID-19 pandemic on global supply chains and its exposures of the vulnerabilities of the This research paper describes the development and implementation of a practical SLAM model for warehouse application. SLAM technology allows autonomous systems to map unknown environments while estimating their own position.
null Anagha J. Choudhary, null Purva K. Patil, null Dishant Shah, null Rupali D. Rode, null Hrushikesh B. Kulkarni   +4 more
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