稠密地图估计是同步定位与地图构建(Simultaneous Localization and Mapping，SLAM)的重要目标。针对经典的深度滤波算法重建精度不高的问题，提出一种基于逆深度滤波的改进单目稠密点云重建方法，在极线搜索阶段通过设置阈值提高效率，通过逆深度高斯滤波器更新后验逆深度概率分布，通过帧内检测剔除外点。实验结果验证改进后的稠密重建算法具有更稠密、更精确的重建效果，且无须GPU加速。
MONOCULAR DENSE MAPPING BASED ON INVERSE-DEPTH FILTER
Dense mapping estimation is an important goal for simultaneous localization and mapping(SLAM).Considering the poor reconstruction accuracy of the depth filtering algorithm,an improved monocular dense point cloud map reconstruction method based on inverse-depth filtering is proposed.This algorithm improved the efficiency in the epipolar search phase by setting threshold,and used the inverse depth Gaussian filter to update posterior inverse depth probability distribution.The outside points were eliminated through intra-frame detection.Experimental results show that the improved dense reconstruction method has denser,more accurate reconstruction effects without GPU acceleration.
Dense point cloudMonocular cameraEpipolar searchSLAM
国家科技期刊平台
NSTL
万方数据
维普
