Abstract
In this study, we propose a method to extract features for graph SLAM using an upward-looking camera mounted in an automated guided vehicle (AGV) in a factory environment. To this end, a novel feature detector and descriptor GALS (geometrical arrangement based on lamps and SURF) is proposed in this study. This algorithm consists of two parts: 1) geometrical arrangement of SURF features around a lamp that is often observed in a factory environment; and 2) a SURF descriptor. The use of the geometrical arrangement of several SURF features provides a more distinguishable feature than the use of a single SURF feature, and thus it can be robustly used for feature matching. As a result of GALS-based graph SLAM, a graph is created and optimized through a Georgia Tech Smoothing and Mapping Library (GTSAM) tool so that the accumulated error due to the slip is minimized. A series of experiments in indoor factory environments showed that the proposed scheme resulted in reliable navigation.
| Original language | English |
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| Title of host publication | ICCAS 2016 - 2016 16th International Conference on Control, Automation and Systems, Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 844-848 |
| Number of pages | 5 |
| ISBN (Electronic) | 9788993215120 |
| DOIs | |
| Publication status | Published - 2017 Jan 24 |
| Event | 16th International Conference on Control, Automation and Systems, ICCAS 2016 - Gyeongju, Korea, Republic of Duration: 2016 Oct 16 → 2016 Oct 19 |
Other
| Other | 16th International Conference on Control, Automation and Systems, ICCAS 2016 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Gyeongju |
| Period | 16/10/16 → 16/10/19 |
Keywords
- factory environments
- monocular SLAM
- upward-looking camera
- Visual features
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering