Feature-based robot navigation using a Doppler-azimuth radar. Issue 4 (3rd April 2017)

• Record Type: Journal Article
• Authors: Guan, Robin P.; Ristic, Branko; Wang, Liuping; Moran, Bill; Evans, Rob
• Is Part Of: International journal of control. Volume 90:Issue 4(2017)
• Page Start: 888
• Page End: 900
• Publication Date: 2017-04-03
• Subjects: Extended Kalman filter -- Cramer–Rao lower bound -- robot self-localisation -- azimuth reading -- Doppler-shift measurement; Automatic control -- Periodicals; Electronic journals; 629.8
• DOI: 10.1080/00207179.2016.1244727
• Languages: English
• ISSNs: 0020-7179

Abstract

ABSTRACT: The merits of the Doppler radar compared to other existing sensors used for robot navigation, such as the LIDAR, include a lower cost, smaller size and lower weight which could prove to be useful in economically building a swarm of mobile vehicles. This paper demonstrates that, given a feature-based map and landmark associations, a Doppler radar which outputs Doppler-shift measurements with associated azimuth readings and an Extended Kalman filter for processing measurements, a robot is able to self-localise. Additionally, the Cramer–Rao lower bound (CRLB) for the estimation error of this scenario is computed to show that it is theoretically feasible for robot self-localisation, which is later verified through Monte–Carlo simulations.