论文标题
快速的Orb-slam没有关键点描述符
Fast ORB-SLAM without Keypoint Descriptors
论文作者
论文摘要
间接的视觉大满贯方法由于对环境变化的鲁棒性而变得越来越流行。 ORB-SLAM2 \ cite {orbslam2}是该域中的基准方法，但是，除非选择帧作为钥匙帧，否则它会消耗大量的计算描述符的时间。为了克服这些问题，我们提出了fastorb-slam，它轻巧有效，因为它在没有计算描述符的情况下跟踪相邻帧之间的关键点。为此，根据稀疏的光流，提出了两阶段的粗到1个描述符独立关键点匹配方法。在第一阶段，我们通过简单但有效的运动模型预测初始关键点对应关系，然后通过基于金字塔的稀疏光流跟踪来鲁棒地建立对应关系。在第二阶段，我们利用运动平滑度和表现几何形状的约束来完善对应关系。特别是，我们的方法仅计算关键帧的描述符。我们在\ textit {tum}和\ textit {icl-nuim} RGB-D数据集上测试fastorb-slam，并将其准确性和效率与9个现有RGB-D SLAM方法进行比较。定性和定量结果表明，我们的方法达到了最先进的准确性，大约是Orb-Slam2的两倍。
Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 \cite{orbslam2} is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is lightweight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two-stage coarse-to-fine descriptor independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on \textit{TUM} and \textit{ICL-NUIM} RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2.
