Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

Rodríguez-Losada, D.; Segundo, Pablo San; Matı́a, Fernando; Pedraza, L.

doi:10.1007/s10846-008-9296-4

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Combining equations (10) and (11) the probability of every point k is calculated using equation (12).…”

Section: Map Updatementioning

confidence: 99%

“…So the importance factor is proportional to the posterior p(z t |s t,i , u t , z t−1 ) which is already calculated in the map update section 2.4 as the denominator of equations (6) or (10).…”

Section: Importance Factor Calculationmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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“…Combining equations (10) and (11) the probability of every point k is calculated using equation (12).…”

Section: Map Updatementioning

confidence: 99%

Section: Importance Factor Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generative Cloud of Points SLAM (G-SLAM)

Zikos¹,

Petridis²

2019

Preprint

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show abstract

“…(3) A particle with a wrongfully high weight causes the depletion problem where the lack of particles in the vicinity of the correct state. When the uncertainty of the robot motion is large and the accuracy of the sensors is high, the depletion problem can occur [19].…”

Section: Analytic Approach To Slammentioning

confidence: 99%

A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization

Kang

Kim

et al. 2010

Appl Intell

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This paper presents Neuro-Evolutionary Optimization SLAM (NeoSLAM) a novel approach to SLAM that uses a neural network (NN) to autonomously learn both a nonlinear motion model and the noise statistics of measurement data. The NN is trained using evolutionary optimization to learn the residual error of the motion model, which is then added to the odometry data to obtain the full motion model estimate. Stochastic optimization is used, to accommodate any kind of cost function. Prediction and correction are performed simultaneously within our neural framework, which implicitly integrates the motion and sensor models. An evolutionary programming (EP) algorithm is used to progressively refine the neural model until it generates a trajectory that is most consistent with the actual sensor measurements. During this learning process, NeoSLAM does not require any prior knowledge of motion or sensor models and shows consistently good performance regardless of the robot and the sensor noise type. Furthermore, NeoSLAM does not require the data association step at loop closing which is crucial in most other SLAM algorithms, but can still generate an accurate map. Experiments in various complex environments with widely-varying types of noise show that the learning capability of NeoSLAM ensures performance that is consistently less sensitive to noise and more accurate than that of other SLAM methods.

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“…Some years later, the FastSlam, an upgrade to the SLAM problem, was presented in [27] The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter.…”

Section: Localizationmentioning

confidence: 99%

Control architecture of a social robot for the navigation and interaction with the environment

Alvarado¹,

Piero²

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Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

Cited by 13 publications

References 13 publications

Generative Cloud of Points SLAM (G-SLAM)

Generative Cloud of Points SLAM (G-SLAM)

A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization

Control architecture of a social robot for the navigation and interaction with the environment

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