Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-05T21:03:05.552Z Has data issue: false hasContentIssue false

Ultrasonic classification and location of 3D room features using maximum likelihood estimation - Part I

Published online by Cambridge University Press:  01 September 1997

Hong Mun-Li
Affiliation:
School of Applied Science, Nanyang Technological University, Singapore
Lindsay Kleeman
Affiliation:
Intelligent Robotics Research Centre, Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria 3168 Australia

Abstract

Current mobile robot ultrasonic localisation techniques use sensor systems which rely on features in a horizontal plane. The implicit assumption is that the room boundary on the horizontal plane is not obstructed by objects such as furniture. This assumption is often not realistic and restricts the versatility and portability of these systems. The solution proposed in this paper is the provision of sensing flexibility to use other 3D room boundaries (e.g. ceiling-wall intersections) as 3D natural beacons. We propose a 3D ultrasonic sensor array that uses a Maximum Likelihood Estimator to match the echo arrival times to different object classes and to determine the location of the 3D target. This method does not require fast data acquisition or powerful computing. It has been implemented on a robot localisation application with the Extended Kalman Filter. This paper is the first of two parts, and presents theoretical results on target classification and minimum transducer requirements. The second part, in the next issue of Robotica, presents experimental results on the characterisation of the sensor and its application to robot localisation, and includes the references for the both papers.

Type
Research Article
Copyright
© 1997 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This work was conducted at the Dept. of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia and was supported by a Monash Graduate Scholarship.