Casualty detection from 3D point cloud data for autonomous ground mobile rescue robots
File(s)
Accepted version
OA Location
Author(s)
Saputra, Roni Permana
Kormushev, Petar
Type
Conference Paper
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 detections 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.
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 detections 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.
Date Issued
2018-09-20
Date Acceptance
2018-06-14
Citation
IEEE International Symposium on Safety, Security, and Rescue Robotics, 2018
Publisher
IEEE
Journal / Book Title
IEEE International Symposium on Safety, Security, and Rescue Robotics
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/8468617
Source
SSRR 2018
Subjects
cs.RO
cs.RO
Publication Status
Published
Start Date
2018-08-06
Finish Date
2018-08-08
Coverage Spatial
Philadelphia, USA
Date Publish Online
2018-09-20