Sim-to-real learning for casualty detection from ground projected point cloud data
File(s)
Accepted version
Author(s)
Saputra, Roni Permana
Rakicevic, Nemanja
Kormushev, Petar
Type
Conference Paper
Abstract
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.
Date Issued
2020-01-27
Date Acceptance
2019-08-13
Citation
Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS 2019), 2020
Publisher
IEEE
Journal / Book Title
Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS 2019)
Copyright Statement
© 2019 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
http://kormushev.com/papers/Saputra_IROS-2019.pdf
Source
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
Subjects
cs.CV
cs.CV
cs.RO
Publication Status
Published
Start Date
2019-11-03
Finish Date
2019-11-08
Coverage Spatial
The Venetian Macao, Macau, China