基于点线特征融合的低纹理单目视觉同时定位与地图构建算法

当图像因相机快速运动造成模糊或者处在低纹理场景时，仅使用点特征的同步定位与地图构建（SLAM）算法难以跟踪提取足够多的特征点，导致定位精度和匹配鲁棒性较差。而如果造成误匹配，甚至系统都无法工作。针对上述问题，提出了一种基于点线特征融合的低纹理单目SLAM算法。首先，加入了线特征来加强系统稳定性，并解决了点特征算法在低纹理场景中提取不足的问题；然后，对点、线特征提取数量的选择引入了加权的思想，根据场景的丰富程度，对点线特征的权重进行了合理分配。所提算法是在低纹理场景下运行的，因而设置以线特征为主、点特征为辅。在TUM室内数据集上的实验结果表明，与现有的点线特征算法相比，所提算法有效地提高了线特征的匹配精度，使得轨迹误差减小了大约9个百分点，也使得特征提取时间减少了30个百分点，使加入的线特征在低纹理场景中发挥出积极有效的作用，提高了数据整体的准确度和可信度。

Low-texture monocular visual simultaneous localization and mapping algorithm based on point-line feature fusion

When the image is blurred due to rapid camera movement or in low-texture scenes， the Simultaneous Localization And Mapping （SLAM） algorithm using only point features is difficult to track and extract enough feature points， resulting in poor positioning accuracy and matching robustness. If it causes false matching， even the system cannot work. To solve the problem， a low-texture monocular SLAM algorithm based on point-line feature fusion was proposed. Firstly， the line features were added to enhance the system stability， and the problem of insufficient extraction of point feature algorithm in low texture scenes was solved. Then， the idea of weighting was introduced for the extraction number selection of point and line features， and the weight of point and line features were allocated reasonably according to the richness of the scene. The proposed algorithm ran in low-texture scenes， so the line features were set as the main features and the point features were set as the auxiliary features. Experimental results on the TUM indoor dataset show that compared with the existing point-line feature algorithms， the proposed algorithm can effectively improve the matching precision of the line features， has the trajectory error reduced by about 9 percentage points， and has the feature extraction time reduced by 30 percentage points. As the result， the added line features play a positive and effective role in low-texture scenes， and improve the overall accuracy and reliability of the data.