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BibTex format

author = {Saputra, RP and Rakicevic, N and Kormushev, P},
publisher = {IEEE},
title = {Sim-to-real learning for casualty detection from ground projected point cloud data},
url = {},
year = {2019}

RIS format (EndNote, RefMan)

AB - This paper addresses the problem of human bodydetection—particularly a human body lying on the ground(a.k.a. casualty)—using point cloud data. This ability to detect acasualty is one of the most important features of mobile rescuerobots, in order for them to be able to operate autonomously.We propose a deep-learning-based casualty detection methodusing a deep convolutional neural network (CNN). This networkis trained to be able to detect a casualty using a point-clouddata input. In the method we propose, the point cloud input ispre-processed to generate a depth image-like ground-projectedheightmap. This heightmap is generated based on the projecteddistance of each point onto the detected ground plane within thepoint cloud data. The generated heightmap—in image form—isthen used as an input for the CNN to detect a human bodylying on the ground. To train the neural network, we proposea novel sim-to-real approach, in which the network model istrained using synthetic data obtained in simulation and thentested on real sensor data. To make the model transferableto real data implementations, during the training we adoptspecific data augmentation strategies with the synthetic trainingdata. The experimental results show that data augmentationintroduced during the training process is essential for improvingthe performance of the trained model on real data. Morespecifically, the results demonstrate that the data augmentationson raw point-cloud data have contributed to a considerableimprovement of the trained model performance.
AU - Saputra,RP
AU - Rakicevic,N
AU - Kormushev,P
PY - 2019///
TI - Sim-to-real learning for casualty detection from ground projected point cloud data
UR -
UR -
ER -