针对基于特征点的视觉同步定位与建图(SLAM)算法在低纹理环境下特征提取能力弱、定位精度降低和鲁棒性差的问 题,本文提出了一种基于稀疏直接法的特征增强视觉 SLAM 算法。 首先对图像序列进行预处理,提高算法的特征提取能力;然 后融合基于图优化的稀疏直接法和特征点法求解位姿,在保证算法定位精度的前提下,提高算法的运行效率和鲁棒性。 由 TUM 数据集的实验结果表明,本文提出的算法定位精度优于当前 SLAM 算法,在 TUM 数据集中纹理稀疏的场景下,该算法提取 的特征点数目是 ORB-SLAM2 算法的 9. 6 倍,平均每帧跟踪耗时减少了 58% 。
To address the problems of weak feature extraction ability, lower positioning accuracy and poor robustness of the feature pointbased visual simultaneous localization and mapping ( SLAM) algorithm in low-texture environment, this article proposes a featureenhanced visual SLAM algorithm based on the sparse direct method. Firstly, the image sequence is preprocessed to improve the feature extraction ability of the algorithm. Then, the pose is solved by combining the sparse direct method based on graph optimization and the feature point method. The operation efficiency and robustness of the algorithm are improved under the premise of ensuring the positioning accuracy of the algorithm. The experimental results of the TUM data set show that the positioning accuracy of the proposed algorithm is better than those of the current SLAM algorithms. In the scenario with sparse texture in the TUM data set, the number of feature points extracted by the algorithm is 9. 6 times more than that of the ORB-SLAM2 algorithm, and the average number of points per frame tracking time is reduced by 58% .
叶俊强,尤 睿,于明鑫,祝连庆,于世洁.基于稀疏直接法的特征增强视觉 SLAM 算法[J].仪器仪表学报,2023,(6):205-212复制