Abstract

Reliable assessment of terrain traversability using multi-sensory input is a key issue fordriving automation, particularly when the domain is unstructured or semi-structured, asin natural environments. In this paper, LIDAR-stereo combination is proposed to detecttraversable ground in outdoor applications. The system integrates two self-learning classi-ers, one based on LIDAR data and one based on stereo data, to detect the broad class ofdrivable ground. Each single-sensor classier features two main stages: an adaptive trainingstage and a classication stage. During the training stage, the classier automaticallylearns to associate geometric appearance of 3D data with class labels. Then, it makes predictionsbased on past observations. The output obtained from the single-sensor classiersare statistically combined in order to exploit their individual strengths and reach an overallbetter performance than could be achieved by using each of them separately. Experimentalresults, obtained with a test bed platform operating in rural environments, are presented tovalidate and assess the performance of this approach, showing its eectiveness and potentialapplicability to autonomous navigation in outdoor contexts.

Autore Pugliese

• Milella Annalisa

Tutti gli autori

• A. Milella; G. Reina; R. Worst

Titolo volume/Rivista

Robotica

Anno di pubblicazione

2016

ISSN

0263-5747

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile