Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms

Tuna, Gürkan; Gülez, Kayhan; Güngör, Vehbi Çağrı; Mumcu, Tarık Veli

doi:10.1109/iecon.2012.6389151

Cited by 31 publications

(12 citation statements)

References 11 publications

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“…The Extended Kalman filter solves the process to be estimated with nonlinear approach. SLAM provides map building and precise localization at the same time [5]. Hence, it requires more memory for complex calculations and data storing.…”

Section: Mecatronics-2014-tokyomentioning

confidence: 99%

Localization and feature recognition: Implementation on an indoor navigator robot

Lye

Nisar

Lai

et al. 2014

2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS2014- Tokyo)

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Oriented FAST and rotated BRIEF, or ORB, is a binary algorithm with rotation invariant and noise resistant abilities. ORB, which was built on the FAST key point detector and the BRIEF descriptor, can be applied for object recognition. This paper studies the feasibility of ORB implementation in indoor navigation with Lego Mindstorms NXT robot. The robot was capacitated to explore and navigate autonomously under a known environment, based on its decision making ability. An Android device was attached to the robot, providing the vision and meanwhile performing complex computation such as an autonomous navigation system. Communication amongst the Android device and the robot was performed with both wireless and Bluetooth transmissions from a computer. In this project, the robot with recognized the landmark using the ORB feature detection. Along with the ultrasonic sensory data, the robot localized itself by utilizing Monte Carlo Localization method. Besides, A* shortest path algorithm was applied to plan the shortest path for the robot to reach the goal. Analysis was made on the collected results and the failure was discussed. Subsequently the conclusion was drawn and some recommendations for improvements were made.

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Section: Mecatronics-2014-tokyomentioning

confidence: 99%

Localization and feature recognition: Implementation on an indoor navigator robot

Lye

Nisar

Lai

et al. 2014

2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS2014- Tokyo)

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“…In the domain of range-only SLAM (RO-SLAM), Fast-SLAM approaches have demonstrated to be one of the most accurate solutions for most online SLAM problems [7]. This framework is used by some authors like [8] where a PF is used for both, localization and mapping problem, using two different techniques to reduce the exponential complexity of PF.…”

Section: Related Workmentioning

confidence: 99%

Decentralized simultaneous localization and mapping for multiple aerial vehicles using range-only sensors

Fabresse

Caballero

Ollero

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an approach for decentralized range-only simultaneous localization and mapping (RO-SLAM) of a network of aerial vehicles and a set of static range-only sensors deployed in the environment. The paper makes use of a multi-hypothesis framework developed by the authors [1] in order to deal with the multiple hypotheses that are present in the early stages of the undelayed RO-SLAM, this paper extends the approach to consider the integration of landmark estimations provided by other aerial vehicles nearby the robot. The method will enable a significant reduction in the convergence time needed to remove wrong localization hypotheses for every range-only landmark and, as a results, a map with improved accuracy. The proposed approach is validated first in simulations and later on with real experiments involving real range-only sensors and two unmanned aerial vehicles.

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“…In the domain of range-only SLAM (RO-SLAM), different SLAM frameworks have been proposed and compared [6], [7], being the Fast-SLAM approach considered the most accurate and efficient solution. This Fast-SLAM framework is used in [8], [9], where a particle filter for both localization and mapping problem is used.…”

Section: Introductionmentioning

confidence: 99%

Robust range-only SLAM for aerial vehicles

Fabresse

Caballero

Maza

et al. 2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a robust method to map the position of a set of radio range sensors while at the same time an aerial vehicle is being localized with respect that map employing only range measurements even in the presence of noisy measurements. The method makes use of a pre-filtering algorithm to detect and remove measurements outliers and extends the original approach presented in [1] to model and estimate the measurement model of each radio sensor in order to correct the computed range of each node. The method is validated in simulation involving an aerial vehicle and radiobased range sensors.

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Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms

Cited by 31 publications

References 11 publications

Localization and feature recognition: Implementation on an indoor navigator robot

Localization and feature recognition: Implementation on an indoor navigator robot

Decentralized simultaneous localization and mapping for multiple aerial vehicles using range-only sensors

Robust range-only SLAM for aerial vehicles

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