LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites

Khashayar Asadi, Pengyu Chen, Kevin Han, Tianfu Wu and Edgar Lobaton
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Khashayar Asadi: Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr, Raleigh, NC 27606, USA
Pengyu Chen: Department of Computer Science, Columbia University in the City of New York, Mudd Building, 500 W 120th St, New York, NY 10027, USA
Kevin Han: Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr, Raleigh, NC 27606, USA
Tianfu Wu: Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Drive, Raleigh, NC 27606, USA
Edgar Lobaton: Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Drive, Raleigh, NC 27606, USA

Data, 2019, vol. 4, issue 1, 1-17

Abstract: An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models’ size and high memory usage associated with real-time segmentation are the main challenges for mobile robotics systems that have limited computing resources. To overcome these challenges, this paper presents an efficient semantic segmentation method named LNSNet (lightweight navigable space segmentation network) that can run on embedded platforms to determine navigable space in real-time. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance. LNSNet is faster, has fewer parameters and less model size, while provides similar accuracy compared to existing models. A new pixel-level annotated dataset for real-time and mobile navigable space segmentation in construction environments has been constructed for the proposed method. The results demonstrate the effectiveness and efficiency that are necessary for the future development of the autonomous robotics systems.

Keywords: efficient real-time segmentation; embedded platform; autonomous navigation in construction; autonomous data collection (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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