Environment awareness through advanced sensing systems is a major requirement for a mobile robot to operate safely, particularly when the environment is unstructured, as in an outdoor setting. In this paper, a multi-sensory approach is proposed for automatic traversable ground detection using 3D range sensors. Specifically, two classifiers are presented, one based on laser data and one based on stereovision. Both classifiers rely on a self-learning scheme to detect the general class of ground and feature two main stages: an adaptive training stage and a classification stage. In the training stage, the classifier learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers is statistically combined exploiting their individual advantages in order to reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in a rural environment, are presented to validate this approach, showing its effectiveness for autonomous safe navigation.
LIDAR and Stereo Imagery Integration for Safe Navigation in Outdoor Settings
REINA, GIULIO;
2013-01-01
Abstract
Environment awareness through advanced sensing systems is a major requirement for a mobile robot to operate safely, particularly when the environment is unstructured, as in an outdoor setting. In this paper, a multi-sensory approach is proposed for automatic traversable ground detection using 3D range sensors. Specifically, two classifiers are presented, one based on laser data and one based on stereovision. Both classifiers rely on a self-learning scheme to detect the general class of ground and feature two main stages: an adaptive training stage and a classification stage. In the training stage, the classifier learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers is statistically combined exploiting their individual advantages in order to reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in a rural environment, are presented to validate this approach, showing its effectiveness for autonomous safe navigation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
