Title :
An ultrasonic/RF GP-based sensor model robotic solution for indoors/outdoors person tracking
Author :
Daobilige Su ; Miro, Jaime Valls
Author_Institution :
Centre for Autonomous Syst., Univ. of Technol., Sydney, NSW, Australia
Abstract :
An non-linear Bayesian regression engine for robotic tracking based on an ultrasonic/RF sensor unit is presented in this paper. The proposed system is able to maintain systematic tracking of a leading human in indoor/outdoor settings with minimalistic instrumentation. Compared to popular camera based localization system the sonar array/RF based system has the advantage of being insensitive to background light intensity changes, a primary concern in outdoor environments. In contrast to single-plane laser range finder based tracking the proposed scheme is able to better adapt to small terrain variations, while at the same time being a significantly more affordable proposition for tracking with a robotic unit. A key novelty in this work is the utilisation of Gaussian Process Regression (GPR) to build a model for the sensor unit, which is shown to compare favourably against traditional linear triangulation approaches. The covariance function yield by the GPR sensor model also provides the additional benefit of outlier rejection. We present experimental results of indoors and outdoors tracking by mounting the sensor unit on a Garden Utility Transportation System (GUTS) robot and compare the proposed approach with linear triangulation which clearly show the inference engine capability to generalise relative localisation of human and a marked improvement in tracking accuracy and robustness.
Keywords :
Bayes methods; Gaussian processes; covariance analysis; mobile robots; regression analysis; target tracking; ultrasonic devices; Garden Utility Transportation System; Gaussian process regression; RF GP-based sensor model robotic solution; covariance function; indoor person tracking; linear triangulation; nonlinear Bayesian regression; outdoor person tracking; ultrasonic GP-based sensor model robotic solution; Ground penetrating radar; Radar tracking; Radio frequency; Robot sensing systems; Sonar measurements;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
DOI :
10.1109/ICARCV.2014.7064565