KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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6D ICP Based on Adaptive Sampling of Color Distribution

색상분포에 기반한 적응형 샘플링 및 6차원 ICP

  • Received : 2015.10.22
  • Accepted : 2016.05.09
  • Published : 2016.09.30
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Abstract

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.

3차원 정합이란 다시점에서 획득한 3차원 점군들을 정렬하는 기술로써 지난 수십 년간 많은 연구가 진행되고 있는 분야이다. 이러한 3차원 정합은 ICP(Iterative Closest Point) 알고리즘을 시작으로 많은 변형 ICP가 소개되고 있다. 하지만 ICP 계열의 알고리즘들은 최근접점을 대응점으로 간주하여 알고리즘을 수행한다. 그렇기 때문에 3차원 점군의 초기 오차가 큰 경우 정확한 대응점 탐색에 실패할 수 있다. 이런 문제점을 해결하기 위해 본 논문에서는 색상과 3차원 거리가 융합된 6차원 거리와 색상분포 유사도를 이용한다. 더 나아가 색상 분할 기반 적응형 샘플링을 이용하여 알고리즘 연산 속도를 감소시키고 성능을 향상시키는 것을 목표로 한다. 마지막으로 실험을 통해 기존의 방법과 본 논문에서 제안하는 방법의 성능을 비교한다.

Keywords

References

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