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A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
- Source :
- ICRA
- Publication Year :
- 2018
-
Abstract
- This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information. We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.<br />7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo video can be found at https://youtu.be/Bkt53dAehjY
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
business.industry
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Image segmentation
020901 industrial engineering & automation
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Computer vision
Artificial intelligence
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICRA
- Accession number :
- edsair.doi.dedup.....1490fb64f31f778c9c218dc82c35af79