This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.

1. 서 론
 2. 관련 연구
 3. 구형 특징 표현(Spherical SignatureDescription)
  3.1 점유 증거 갱신을 위한 준비
  3.2 점유 증거 갱신
 4. 환경 형상 학습(Environmental FeatureLearning)
  4.1 제한된 볼츠만 머신(Restricted BolzmannMachine)
  4.2 심층 신뢰 신경망(Deep Belief Nets)
 5. 실험 결과
  5.1 실험 시스템 및 데이터
  5.2 구형 특징 표현 결과
  5.3 환경 형상 학습 결과
 6. 결 론
 References

• 이세진(공주대학교 기계자동차공학부) | Sejin Lee Corresponding Author
• 김동현(Kennesaw State University) | Donghyun Kim