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정제 모듈을 포함한 컨볼루셔널 뉴럴 네트워크 모델을 이용한라이다 영상의 분할
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- Issue Date
- 2018-03
- Citation
- 로봇학회논문지, v.13, no.1, pp.8-15
- ISSN
- 1975-6291
- Publisher
- 한국로봇학회(KROS)
- Language
- Korean
- Type
- Journal Article
- Abstract
- This paper proposes a convolutional neural network model for distinguishing areas occupied by obstacles from a LiDAR image converted from a 3D point cloud. The channels of a LiDAR image used as input consist of the distances to 3D points, the reflectivities of 3D points, and the heights of 3D points from the ground. The proposed model uses a LiDAR image as an input and outputs a result of a segmented LiDAR image. The proposed model adopts refinement modules with skip connections to segment a LiDAR image. The refinement modules with skip connections in the proposed model make it possible to construct a complex structure with a small number of parameters than a convolutional neural network model with a linear structure. Using the proposed model, it is possible to distinguish areas in a LiDAR image occupied by obstacles such as vehicles, pedestrians, and bicyclists. The proposed model can be applied to recognize surrounding obstacles and to search for safe paths.
- KSP Keywords
- 3D point cloud, Complex structure, Convolution neural network(CNN), Linear structure, Proposed model, Safe path, neural network model, skip connections