Diversity in Pedestrian Safety for Industrial Environments Using 3D Lidar Sensors and Neural Networks*Research supported by the New Zealand Ministry for Business Innovation and Employment (MBIE) on contract UOAX1414
Jamie Bell, Bruce A. MacDonald, Ho Seok Ahn
Abstract
The motivation of the work presented here is to create a component of a safety system based on 3D lidar sensors, specifically for industrial environments where some rules can be set for people who will be in close proximity to working robots. Specifically, the operating procedure that is put in place in the workplace is that all people must wear the provided high visibility clothing, which has retro-reflective strips attached. It is shown here that the retro-reflective strips provide a strong cue for pedestrian detection in the intensity data from a lidar sensor within a range of 4 metres. We present and compare multiple methods of exploiting this cue and provide a recommendation for how a safety system should be architected in order to best exploit the lidar intensity data in combination with more common approaches for detection of objects from the lidar range data. Amongst these detection methods is the use of neural networks, which present challenges for key components of standardized safety system development-in particular, for programming methodology control, interpretability of testing and diagnostic coverage. We propose methods for how to start to address these challenges and how to integrate neural networks into safety systems.
BibTeX
@inproceedings{iros2018_diversityinpedes,
title = {Diversity in Pedestrian Safety for Industrial Environments Using 3D Lidar Sensors and Neural Networks*Research supported by the New Zealand Ministry for Business Innovation and Employment (MBIE) on contract UOAX1414},
author = {Jamie Bell and Bruce A. MacDonald and Ho Seok Ahn},
booktitle = {IROS 2018},
year = {2018}
}