Results 121 to 130 of about 3,988 (156)

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 ...
Delphine Dufourd, Raja Chatila 0001, Dominique Luzeaux   +2 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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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.
Cheng-Kai Yang, Chen-Chien James Hsu, 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 0001, Wen Yu 0001, Erik Zamora 0001   +2 more
openaire   +1 more source

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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A solution to the simultaneous localization and map building (SLAM) problem

IEEE Transactions on Robotics and Automation, 2001
The 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, Paul M. Newman, Steve Clark, Hugh F. Durrant-Whyte, M. Csorba   +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, 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 J. Hsu, Wei-Yen Wang, Yin-Tien Wang   +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), 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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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), 2005
This 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, Stergios I. Roumeliotis   +1 more
openaire   +1 more source