论文标题
FD-SLAM：使用功能和密集匹配的3-D重建
FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
论文作者
论文摘要
众所周知，基于密集匹配的视觉大满贯系统在本地准确，但也容易受到长期漂移和映射损坏的影响。相比之下，功能匹配方法可以实现更大的长期一致性，但是当特征信息稀疏时，局部姿势估计可能不准确。基于这些观察结果，我们提出了一个RGB-D SLAM系统，该系统利用这两种方法的优势：使用密集的框架到模型的探光仪来构建准确的子图和跨子图跨亚图以进行全局地图优化的基于特征的匹配。此外，我们还基于3D功能结合了基于学习的循环封闭组件，该功能进一步稳定了地图构建。我们已经评估了公共数据集的室内序列的方法，结果表明，在地图重建质量和姿势估计方面，它在标准杆上或比最新系统更好。该方法还可以扩展到其他系统经常失败的大型场景。
It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption. In contrast, feature matching methods can achieve greater long-term consistency but can suffer from inaccurate local pose estimation when feature information is sparse. Based on these observations, we propose an RGB-D SLAM system that leverages the advantages of both approaches: using dense frame-to-model odometry to build accurate sub-maps and on-the-fly feature-based matching across sub-maps for global map optimisation. In addition, we incorporate a learning-based loop closure component based on 3-D features which further stabilises map building. We have evaluated the approach on indoor sequences from public datasets, and the results show that it performs on par or better than state-of-the-art systems in terms of map reconstruction quality and pose estimation. The approach can also scale to large scenes where other systems often fail.
