一种改进的RGB-D SLAM室内空间三维重建方法

提出一种改进的基于ORB(Oriented FAST and Rotated BRIEF)的RGB-D SLAM室内三维重建方法。前端利用改进的RANSAC(Random Sample Consensus)算法提升特征点匹配精度,结合PnP(Perspective-n-Point)实现点云图像的精确配准；后端采用位姿图进行优化,降低噪声数据对重建的影响；并利用回环检测控制重建过程中的误差累积。实验结果表明,所提出的特征点匹配方法能显著提高特征点的匹配精度,正确匹配率约为94%,较传统RANSAC算法提升6.5%；所提方法与传统RGB-D SLAM重建方法相比,重建结果质量更优,其中相机估计轨迹与真实轨迹互差RMS结果更佳,RMS值均小于0.08m。

An Improved Approach of Indoor Space Three-dimensional Reconstruction with RGB-D SLAM

An Improved Approach of indoor space three-dimensional reconstruction with RGB-D SLAM based on ORB (oriented FAST and rotated BRIEF) is proposed. The front end uses the improved RANSAC (Random Sample Consensus) algorithm to improve the feature point matching accuracy, combined with PNP (Perspective-n-point) algorithm to realize the accurate registration of point cloud image, and the back end adopts pose graph to optimize to reduce the influence of noise data on reconstruction, and the error accumulation in the reconstruction process is controlled by using loop detection. Experimental results show that the proposed feature point matching method can significantly improve the matching accuracy of feature points, the correct matching rate is about 94%, which is 6.5% higher than that of traditional RANSAC algorithm, and the proposed reconstruction method is better than the traditional RGB-D SLAM, and the quality of reconstruction results is more excellent. The RMS result of the camera estimated trajectory and the real trajectory is better., and the RMS values are all less than 0.08m.