가우시안 프로세스를 이용한 실내 환경에서 소형무인기에 적합한 SLAM 시스템 개발

다국어 초록 (Multilingual Abstract)

Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.

목차 (Table of Contents)

• Abstract
• Ⅰ. 서론
• Ⅱ. 기본적인 이론과 알고리즘
• Ⅲ. SLAM 시스템
• Ⅳ. 실험 및 분석
• Ⅴ. 결론
• REFERENCES

참고문헌 (Reference)

1 J. O. Lee, "Vision-based indoor localization for unmanned aerial vehicles" 24 (24): 373-377, 2011

2 I. W. Selesnick, "The dual-tree complex wavelet transform" 22 (22): 123-151, 2005

3 A. Brooks, "Gaussian process models for indoor and outdoor sensor-centric ro-bot localization" 24 (24): 1341-1351, 2008

4 C. E. Rasmussen, "Gaussian Processes for Machine Learning" the MIT Press 2006

5 J. Huang, "Efficient, generalized indoor WiFi GraphSLAM" 1038-1043, 2011

6 C. Han, "Distinctive image features from scale-invariant keypoints" 60 (60): 91-110, 2004

7 E. Altuğ, "Control of a quadrotor helicopter using visual feedback" 2002

8 T. T. Nwe, "Application of an inertial navigation system to the quad-rotor UAV using MEMS sensors" 2008

9 Y. Xu, "Adaptive sampling for leanrning gaussian process using mobile sensor networks" 11 (11): 3051-3066, 2011