Genetic Algorithm-Based Optimization for Color Point Cloud Registration

Point cloud registration is an important technique for 3D environment map construction. Traditional point cloud registration algorithms rely on color features or geometric features, which leave problems such as color affected by environmental lighting. This article introduced 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 made the HSV distribution of the tangent plane continuous, and we used the genetic algorithm to optimize the point cloud color information consistently. The Gauss–Newton method was utilized to realize the optimal registration of color point clouds for the joint error function of color and geometry. The contribution of this study was that the genetic algorithm was used to optimize HSV color information of the point cloud and was 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 showed that the square error of color information saturation and lightness optimized by the genetic algorithm was reduced by 14.07% and 37.16%, respectively. The color point cloud registration algorithm in this article was reduced by 12.53% on average compared with the optimal result algorithm RMSE.