Abstract

Autonomous off-road ground vehicles require advanced perception systems in order to sense and understand the surrounding environment, while ensuring robustness under compromised visibility conditions. In this paper, the use of millimeter wave radar is proposed as a possible solution for all-weather off-road perception. A self-learning ground classifier is developed that segments radar data for scene understanding and autonomous navigation tasks. The proposed system comprises two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate appearance of radar data with class labels. Then, it makes predictions based on past observations. The training set is continuously updated online using the latest radar readings, thus making it feasible to use the system for long range and long duration navigation, over changing environments. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate this approach. Conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.

Autore Pugliese

• Reina Giulio

Tutti gli autori

• G. Reina , A. Milella , J. Underwood

Titolo volume/Rivista

SPRINGER TRACTS IN ADVANCED ROBOTICS

Anno di pubblicazione

2014

ISSN

1610-7438

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

0

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile