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- W2801696468 abstract "In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes." @default.
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- W2801696468 date "2018-01-01" @default.
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- W2801696468 title "Adaptive particle filter for localization problem in service robotics" @default.
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- W2801696468 doi "https://doi.org/10.1051/matecconf/201816101004" @default.
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