Work-in-Progress: Enabling Edge-based Self-Navigation in Earthquake-Struck Zones
Publication Date
9-20-2020
Document Type
Conference Proceeding
Publication Title
2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)
DOI
10.1109/CODESISSS51650.2020.9244030
First Page
37
Last Page
39
Abstract
The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as cracks, puddles, and debris on the streets. In this paper, we present an edge-based self-navigation vehicle that can detect unique obstacles in earthquake-struck sites and discuss the performance and energy impact of various neural network structures, edge platforms, and optimizations. To enable vehicles to safely navigate earthquake-struck sites, we compiled a new image database of various earthquake impacted regions and developed semantic segmentation models that identify obstacles unique to earthquake-sites. The models are tested on an edge-based car platform. To our best knowledge, this is the first study that identifies unique challenges and discusses the performance and energy impact of edge-based self-navigation vehicles for earthquake-struck zones.
Keywords
autonomous navigation, convolutional neural network, edge computing, semantic segmentation
Department
Computer Engineering
Recommended Citation
Ryan Zelek, Vignesh K. Venkateshwar, Sai K. Duggineni, Renu Dighe, and Hyeran Jeon. "Work-in-Progress: Enabling Edge-based Self-Navigation in Earthquake-Struck Zones" 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) (2020): 37-39. https://doi.org/10.1109/CODESISSS51650.2020.9244030
