A Comparative Study of Recent Real Time Semantic Segmentation Algorithms for Visual Semantic SLAM
- Resource Type
- Conference
- Authors
- Javed, Zeeshan; Kim, Gon-Woo
- Source
- 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) Big Data and Smart Computing (BigComp), 2020 IEEE International Conference on. :474-476 Feb, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Semantics
Image segmentation
Real-time systems
Simultaneous localization and mapping
Classification algorithms
Visualization
Computer architecture
vSLAM, Deep Learning, Semantic Segmentation, Convolutional Neural Network
- Language
- ISSN
- 2375-9356
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.