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:2020:10.1109/iros40897.2019.8967642,
author = {Saputra, RP and Rakicevic, N and Kormushev, P},
doi = {10.1109/iros40897.2019.8967642},
publisher = {IEEE},
title = {Sim-to-real learning for casualty detection from ground projected point cloud data},
url = {http://dx.doi.org/10.1109/iros40897.2019.8967642},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - This paper addresses the problem of human body detection-particularly a human body lying on the ground (a.k.a. casualty)-using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We propose a deep-learning-based casualty detection method using a deep convolutional neural network (CNN). This network is trained to be able to detect a casualty using a point-cloud data input. In the method we propose, the point cloud input is pre-processed to generate a depth image-like ground-projected heightmap. This heightmap is generated based on the projected distance of each point onto the detected ground plane within the point cloud data. The generated heightmap-in image form-is then used as an input for the CNN to detect a human body lying on the ground. To train the neural network, we propose a novel sim-to-real approach, in which the network model is trained using synthetic data obtained in simulation and then tested on real sensor data. To make the model transferable to real data implementations, during the training we adopt specific data augmentation strategies with the synthetic training data. The experimental results show that data augmentation introduced during the training process is essential for improving the performance of the trained model on real data. More specifically, the results demonstrate that the data augmentations on raw point-cloud data have contributed to a considerable improvement of the trained model performance.
AU - Saputra,RP
AU - Rakicevic,N
AU - Kormushev,P
DO - 10.1109/iros40897.2019.8967642
PB - IEEE
PY - 2020///
TI - Sim-to-real learning for casualty detection from ground projected point cloud data
UR - http://dx.doi.org/10.1109/iros40897.2019.8967642
UR - http://kormushev.com/papers/Saputra_IROS-2019.pdf
UR - http://hdl.handle.net/10044/1/72557
ER -