# Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

## TL;DR

This paper introduces a novel method for autonomous casualty detection in rescue robots using 3D point cloud data, projecting it onto the ground plane and converting it into a grid-map for shape detection, validated with experiments on a rescue robot.

## Contribution

The paper presents a new approach for casualty detection from 3D point clouds by ground plane projection and grid-map conversion, enhancing robustness to different camera angles and positions.

## Key findings

- Successfully detects casualties in various positions and orientations.
- Robust to different camera angles and placements.
- Effective in real rescue robot scenarios.

## 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.