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.
Highlights
Service robotics market is estimated to almost triple by 2022 from the 2016 level [1]
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
In this paper we focus on the localization problem of a mobile platform with sonar sensors
Summary
Service robotics market is estimated to almost triple by 2022 from the 2016 level [1]. In comparison to industrial robotics, service robots are inexpensive, built from low-cost hardware and need to fulfil softer requirements for accuracy, repeatability and reliability. For Simultaneous Localization and Mapping applications (SLAM) in the industry traditionally optical systems, such as 2D/3D cameras and LIDAR sensors, are used. Both types of the sensors are not beneficial in the light of the above requirements, cameras potentially threatening customer privacy. In this paper we focus on the localization problem of a mobile platform with sonar sensors
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