This paper presents a radar-vision classification approach to segment the visual scene into ground and nonground regions. The proposed system features two main phases: a radar-supervised training phase and a visual classification phase. The training stage relies on a radar-based classifier to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground is used for image segmentation. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate the proposed system.
Radar-Vision Integration for Self-Supervised Scene Segmentation
REINA, GIULIO;
2012-01-01
Abstract
This paper presents a radar-vision classification approach to segment the visual scene into ground and nonground regions. The proposed system features two main phases: a radar-supervised training phase and a visual classification phase. The training stage relies on a radar-based classifier to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground is used for image segmentation. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate the proposed system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
