Journal
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020)
Volume -, Issue -, Pages 474-476Publisher
IEEE
DOI: 10.1109/BigComp48618.2020.00-22
Keywords
vSLAM; Deep Learning; Semantic Segmentation; Convolutional Neural Network
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Funding
- Ministry of Trade, Industry and Energy (MOTIE)
- Korea Institute for Advancement of Technology (KIAT) through the International Cooperative RD program [P0004631]
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2018006154]
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Visual Simultaneous Localization and Mapping (vSLAM) has gained much attention for localization and mapping of autonomous vehicle and many impressive and robust vSLAM systems have been developed and achieved considerable performance in recent years. However, some problem have still not been solved because of limited information from geometrical features. In this paper we provide a comparative analysis of computationally effective pixel-wise semantic segmentation algorithms that can be used in visual semantic SLAM.
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