Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network

Panah, Omid; Panah, Amir; Panah, Amin; Fallahpour, Samere

doi:10.17485/ijst/2015/v8i20/52470

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The same approach was extended by using another type of neural network in [15]. Similar work is presented in [16] and [17] where they both propose to enhance filter-based SLAM with neural networks. K.Choi et al [16] use a Multilayer Perceptron (MLP) neural network with an EKF-SLAM.…”

Section: State Of the Artmentioning

confidence: 97%

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2D SLAM Correction Prediction in Large Scale Urban Environments

Alsayed¹,

Bresson²,

Verroust-Blondet³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Simultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations.

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Section: State Of the Artmentioning

confidence: 97%

“…K.Choi et al [16] use a Multilayer Perceptron (MLP) neural network with an EKF-SLAM. In contrast, Panah et al [17] use a Radial Basis Function (RBF) neural network together with a UKF-SLAM approach.…”

Section: State Of the Artmentioning

confidence: 99%