Title: Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots,
Abstract: One of the most important features of mobile rescue robots is the ability to
autonomously detect casualties, i.e. human bodies, which are usually lying on
the ground. This paper proposes a novel method for autonomously detecting
casualties lying on the ground using obtained 3D point-cloud data from an
on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue
robot. In this method, the obtained 3D point-cloud data is projected onto the
detected ground plane, i.e. floor, within the point cloud. Then, this projected
point cloud is converted into a grid-map that is used afterwards as an input
for the algorithm to detect human body shapes. The proposed method is evaluated
by performing detection of a human dummy, placed in different random positions
and orientations, using an on-board RGB-D camera on a mobile rescue robot
called ResQbot. To evaluate the robustness of the casualty detection method to
different camera angles, the orientation of the camera is set to different
angles. The experimental results show that using the point-cloud data from the
on-board RGB-D camera, the proposed method successfully detects the casualty in
all tested body positions and orientations relative to the on-board camera, as
well as in all tested camera angles.