Genetic Algorithm-Based Optimization for Color Point Cloud Registration - Liu, Dongsheng; Hong, Deyan; Wang, Siting; Chen, Yahui (Frontiers in Bioengineering and Biotechnology, 2022)
Point cloud registration is an important technique for the 3D environment map construction. Traditional point cloud registration algorithms rely on color features or geometric features, which leaves problems such as color affected by environmental lighting. This paper introduces a color point cloud registration algorithm optimized by a genetic algorithm, which has good robustness for different lighting environments. We extracted the HSV color data from the point cloud color information and make the HSV distribution of the tangent plane continuous, and used the genetic algorithm to optimize the point cloud color information consistently. The Gauss-Newton method is utilized to realize the optimal registration of color point clouds for the joint error function of color and geometry. The contribution of this study is that the genetic algorithm is used to optimize HSV color information of the point cloud and applied to the point cloud registration algorithm, which reduces the influence of illumination on color information and improves the accuracy of registration. The experimental results show that the square error of color information saturation and lightness optimized by the genetic algorithm is reduced by 14.07% and 37.16% respectively. And the color point cloud registration algorithm in this paper is reduced by 12.53% on average compared with the optimal result algorithm RMSE.