Fast and Accurate Computation of Surface Normals from Range Images
The fast and accurate computation of surface normals from a point cloud is a critical step for many 3D robotics and automotive problems, including terrain estimation, mapping, navigation, object segmentation, and object recognition. To obtain the tangent plane to the surface at a point, the traditional approach applies total least squares to its small neighborhood. However, least squares becomes computationally very expensive when applied to the millions of measurements per second that current range sensors can generate. We reformulate the traditional least squares solution to allow the fast computation of surface normals, and propose a new approach that obtains the normals by calculating the derivatives of the surface from a spherical range image. Furthermore, we show that the traditional least squares problem is very sensitive to range noise and must be normalized to obtain accurate results. Experimental results with synthetic and real data demonstrate that our proposed method is not only more ef.cient by up to two orders of magnitude, but provides better accuracy than the traditional least squares for practical neighborhood sizes.
H. Badino D. Huber Y. Park T. Kanade
Robotics Institute,Carnegie Mellon University,Pittsburgh,PA,USA Agency for Defence Development,Daejeon,Korea
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
3084-3091
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）