基于半直接法SLAM的大场景稠密三维重建系统

当前三维重建系统大多基于特征点法和直接法的同时定位与地图重建(SLAM)系统,特征点法SLAM难以在特征点缺失的地方具有较好的重建结果,直接法SLAM在相机运动过快时难以进行位姿估计,从而造成重建效果不理想.针对上述问题,文中提出基于半直接法SLAM的大场景稠密三维重建系统.通过深度相机(RGB-D相机)扫描,在特征点丰富的区域使用特征点法进行相机位姿估计,在特征点缺失区域使用直接法进行位姿估计,减小光度误差,优化相机位姿.然后使用优化后较准确的相机位姿进行地图构建,采用面元模型,应用构建变形图的方法进行点云的位姿估计和融合,最终获得较理想的三维重建模型.实验表明,文中系统可适用于各个场合的三维重建,得到较理想的三维重建模型.

Large Scene Dense 3D Reconstruction System Based on Semi-direct SLAM Method

The 3D reconstruction system is mostly based on the simultaneous localization and mapping(SLAM) system of the feature point method and the direct method. The SLAM of feature point method cannot obtain good reconstruction results in the absence of feature points, while the SLAM of direct method has difficulty in estimating the pose with a fast-moving camera, and consequently, reconstruction results are unsatisfactory. To solve these problems, a dense 3D scene reconstruction system with a depth camera (RGB-D camera) based on semi-direct SLAM is proposed in this paper. The feature point method is exploited to estimate the camera pose in feature-rich areas. In the area of missing feature points, the direct method is utilized to estimate the pose of the camera. Then, the three-dimensional map is constructed by the optimized camera pose. The furfel model and the deformation map are utilized to estimate the pose of the point cloud and fuse point cloud. Finally, the ideal 3D reconstruction model is obtained. Experiments show that the system can be applied to all three-dimensional reconstruction of various occasions and acquire the ideal three-dimensional reconstruction model.