  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.
