Simultaneous localization and mapping for mobile robot based on an improved particle filter algorithm

Wang, Zhong Min; Miao, De Hua; Du, Zhi Jiang

doi:10.1109/icma.2009.5246103

Cited by 14 publications

(11 citation statements)

References 7 publications

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“…7 respectively. The errors of the PF-SLAM algorithm are compared with general SLAM algorithm based on PF algorithm in [4], which separates localization and mapping. From Fig.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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Simultaneous localization and mapping embedded with particle filter algorithm

Wang

2016

2016 10th European Conference on Antennas and Propagation (EuCAP)

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In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated with particle filtering and a weighted averaging algorithm respectively, and are linked through an additional feedback and correction process. An adaptive re-sampling method is used to reduce the computational load. The results of the simulation indicate that the PF-SLAM algorithm can localize the robot and landmarks accurately, and the error of landmarks' estimation converges better than general SLAM algorithms.

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“…7 respectively. The errors of the PF-SLAM algorithm are compared with general SLAM algorithm based on PF algorithm in [4], which separates localization and mapping. From Fig.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…As a direct consequence, the efficiency of SLAM becomes very low. Zhong Min Wang proposed an improved Rao-Blackwellised particle filter (IRBRPF) algorithm in [4]. By estimating the robot's posture and landmarks in the environment map separately with PF algorithm, both localization of platform and mapping are achieved.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous localization and mapping embedded with particle filter algorithm

Wang

2016

2016 10th European Conference on Antennas and Propagation (EuCAP)

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“…This Fast-SLAM framework is used in [8], [9], where a particle filter for both localization and mapping problem is used. Two different optimization techniques for the particle filter used to locate the different beacons are applied.…”

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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“…In that paper it is shown how the unscented Fast-SLAM presents better results over other classical methods based on EKF or UKF. In [5] and [6], a FastSLAM solution is proposed using a particle filter for robot localization and other particle filter for each feature (i.e. for each radio beacon).…”

Section: Introductionmentioning

confidence: 99%

“…In [5] an optimization on the particle filter is proposed to estimate the feature position without needing much hypotheses and hence reducing the computational complexity. On the other hand, [6] optimize the problem using an adaptive resampling method for features position estimation. Another FastSLAM solution is considered in [7] and [8], where the authors use a particle filter to initialize an EKF filter for each map feature of the FastSLAM.…”

Section: Introductionmentioning

confidence: 99%

Undelayed 3D RO-SLAM based on Gaussian-mixture and reduced spherical parametrization

Fabresse

Caballero

Maza

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents an undelayed range-only simultaneous localization and mapping (RO-SLAM) based on the Extended Kalman filter. The approach is optimized for working in 3D scenarios, reducing the required computational payload at two levels: first, using a reduced spherical state vector parametrization and, second, proposing a new EKF update scheme. The paper proposes a state vector parametrization based on Gaussian-Mixture to cope with the multi-modal nature of range-only measurements and a reduced spherical parametrization of the range sensor positions that allows to shorten the length of the state vector for a given number of hypotheses. The approach is firstly tested and discussed in simulation, followed by experimental results involving a real robot and radio-based range sensors.

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Simultaneous localization and mapping for mobile robot based on an improved particle filter algorithm

Cited by 14 publications

References 7 publications

Simultaneous localization and mapping embedded with particle filter algorithm

Simultaneous localization and mapping embedded with particle filter algorithm

Robust range-only SLAM for aerial vehicles

Undelayed 3D RO-SLAM based on Gaussian-mixture and reduced spherical parametrization

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