基于Kinect的环境平面特征提取与重构

针对三维场景物体特征识别过程中数据量大、算法复杂等问题,提出一种基于Kinect的环境平面特征提取与重构算法。首先,针对场景的点云分割,采用融合场景几何信息和颜色信息的随机采样一致性(RANSAC)算法,综合二者分割优势,克服几何特征分割过程中分割不足或者过分割,提高分割精度;其次,根据投影变换原理推导出相应的三维坐标变换矩阵,指导分割后独立区域内的三维平面特征信息到二维空间映射,利用凸包概念搜索物体边界信息,实现二维空间的轮廓点提取;最后,通过旋转逆变换,恢复轮廓点的三维信息,完成环境特征重构。采用3组场景数据验证所提算法,实验结果表明,所提算法分割较精确,不容易产生过分割的情况,对不同形状特征的物体,具有较好的重构效果。

Feature extraction and reconstruction of environmental plane based on Kinect

Aiming at the problem of the large amount of data and the complicated algorithm in 3D scene feature recognition process, an feature extraction and reconstruction of environmental plane algorithm based on Kinect was proposed. Firstly, a method of RANdom SAmple Consensus (RANSAC) environment segmentation with the combination of geometrical and color information was proposed, which overcame the over segmentation and lack-segmentation based on geometric characteristic and improved the accuracy. Secondly, according to the principle of perspective projection, the three-dimensional transformation matrix was derived, which guided the 3D environment mapped into a plane. The extraction of contour points in two-dimensional space was realized by searching object boundary information which used convex hull concept. Finally, the 3D information of the contour points was recovered by the rotating inverse transform and the reconstruction of environment features was completed. Three groups of scene data were used to verify the algorithm and the experimental results show the proposed algorithm gains more precise segmentation, reduces over segmentation phenomena, and also has better reconstruction effect for objects with different shape features.