Imperial College London

DrPetarKormushev

Faculty of EngineeringDyson School of Design Engineering

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 9235p.kormushev Website

 
 
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Location

 

25 Exhibition Road, 3rd floor, Dyson BuildingDyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Saputra:2018:10.1109/SSRR.2018.8468617,
author = {Saputra, RP and Kormushev, P},
doi = {10.1109/SSRR.2018.8468617},
publisher = {IEEE},
title = {Casualty detection from 3D point cloud data for autonomous ground mobile rescue robots},
url = {http://dx.doi.org/10.1109/SSRR.2018.8468617},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - One of the most important features of mobilerescue robots is the ability to autonomously detect casualties,i.e. human bodies, which are usually lying on the ground. Thispaper proposes a novel method for autonomously detectingcasualties lying on the ground using obtained 3D point-clouddata from an on-board sensor, such as an RGB-D camera ora 3D LIDAR, on a mobile rescue robot. In this method, theobtained 3D point-cloud data is projected onto the detectedground plane, i.e. floor, within the point cloud. Then, thisprojected point cloud is converted into a grid-map that isused afterwards as an input for the algorithm to detecthuman body shapes. The proposed method is evaluated byperforming detections of a human dummy, placed in differentrandom positions and orientations, using an on-board RGB-Dcamera on a mobile rescue robot called ResQbot. To evaluatethe robustness of the casualty detection method to differentcamera angles, the orientation of the camera is set to differentangles. The experimental results show that using the point-clouddata from the on-board RGB-D camera, the proposed methodsuccessfully detects the casualty in all tested body positions andorientations relative to the on-board camera, as well as in alltested camera angles.
AU - Saputra,RP
AU - Kormushev,P
DO - 10.1109/SSRR.2018.8468617
PB - IEEE
PY - 2018///
TI - Casualty detection from 3D point cloud data for autonomous ground mobile rescue robots
UR - http://dx.doi.org/10.1109/SSRR.2018.8468617
UR - https://ieeexplore.ieee.org/document/8468617
UR - http://hdl.handle.net/10044/1/62194
ER -