L-SLAM: Reduced dimensionality FastSLAM algorithms

Petridis, V.; Zikos, Nikos

doi:10.1109/ijcnn.2010.5596338

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Examples of this type of algorithm include the extended Kalman filter and particle filter. [3] Scan matching techniques do not perform any kind of landmark recognition. Instead, a set of range data values to nearby objects are measured during each position update.…”

Section: Literature Surveymentioning

confidence: 99%

Simultaneous localization and mapping using a micro-particle swarm optimization

Monfredo

Sahin

2015

2015 10th System of Systems Engineering Conference (SoSE)

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Scan matching is a popular way of calculating a robot's position given range data corresponding to objects in the environment. This paper proposes a simultaneous localization and mapping algorithm that uses micro-particle swarm optimization as an alternative method to the traditional scan matching algorithms. The effectiveness of this algorithm is tested and compared to other popular simultaneous and localization algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Simultaneous localization and mapping using a micro-particle swarm optimization

Monfredo

Sahin

2015

2015 10th System of Systems Engineering Conference (SoSE)

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“…EKF-SLAM was extensively used in the past [2], but since computational cost increases significantly with the number of features new methods proposed to overcome this problem. Many probabilistic approaches have been proposed [3] including the Montemerlo's et al solution to stochastic SLAM, FastSLAM 1.0 and 2.0 [4][5][6][7], Grid-based SLAM [8,9], Dual-FastSLAM [10], DP-SLAM [11], L-SLAM [12,13], etc.…”

Section: Introductionmentioning

confidence: 99%

Generative Cloud of Points SLAM (G-SLAM)

Zikos¹,

Petridis²

2019

Preprint

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Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajectory) is estimated by an particle filters while the cloud of points is estimated by an adaptive recursive algorithm which is presented. Main feature of the presented method is the adaptive repositioning of the scattered points and their convergence around obstacles. In addition to our previous publications, In this paper a mathematical derivation of the method is presented and real case scenarios are discussed, presented and compared with other methods.

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“…The Extended Kalman Filter (EKF) was extensively used in the SLAM problem [2], but it has the disadvantage that the computational cost increases significantly with the number of features. Since then, many probabilistic approaches have proposed [3] including the Montemerlo's et al solution to stochastic SLAM, FastSLAM 1.0 and 2.0 [4,5,6,7], Grid-based SLAM [8,9], Dual-FastSLAM [10], DP-SLAM [11], L-SLAM [12,13], etc.…”

Section: Introductionmentioning

confidence: 99%

Generative Simultaneous Localization and Mapping (G-SLAM)

Zikos¹,

Petridis²

2016

Preprint

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Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probability that states the existence of an obstacle in the subsequent point in space. A probabilistic approach with particle filters for the robot's pose estimation and an adaptive recursive algorithm for the map's probability distribution estimation is presented. Key feature of the G-SLAM method is the adaptive repositioning of the scattered points and their convergence around obstacles. In this paper the goal is to estimate the best robot trajectory along with the probability distribution of the obstacles in space. For experimental purposes a four wheel rear drive car kinematic model is used and results derived from real case scenarios are discussed.

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L-SLAM: Reduced dimensionality FastSLAM algorithms

Cited by 10 publications

References 8 publications

Simultaneous localization and mapping using a micro-particle swarm optimization

Simultaneous localization and mapping using a micro-particle swarm optimization

Generative Cloud of Points SLAM (G-SLAM)

Generative Simultaneous Localization and Mapping (G-SLAM)

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