In order to operate autonomously, mobile rescue robots need
to be able to detect human casualties in disaster situations. In this paper,
we propose a novel method for autonomous detection of casualties lying
down on the ground based on point-cloud data. This data can be obtained
from different sensors, such as an RGB-D camera or a 3D LIDAR sensor.
The method is based on a ground-projected point-cloud (GPPC) image
to achieve human body shape detection. A preliminary experiment has
been conducted using the RANSAC method for floor detection and, the
HOG feature and the SVM classifier to detect human body shape. The
results show that the proposed method succeeds to identify a casualty
from point-cloud data in a wide range of viewing angles.