泡泡一分钟:LDSO: Direct Sparse Odometry with Loop Closure

LDSO: Direct Sparse Odometry with Loop Closure

LDSO: 具有回环检测的直接稀疏里程计

Xiang Gao, Rui Wang, Nikolaus Demmel and Daniel Cremers

https://pan.baidu.com/s/1le875_z_ZsrnxwTwwcOftQ

Abstract— In this paper we present an extension of Direct Sparse Odometry (DSO) [1] to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This re-peatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach.Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. These constraints are fused with a co-visibility graph of relative poses extracted from DSO’s sliding window optimization. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift, resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment.

在本文中，我们将直接稀疏里程计（DSO）[1]扩展到具有回环检测和位姿图优化（LDSO）的单目视觉SLAM系统。作为一种直接技术，DSO可以利用具有足够强度梯度的任何图像像素，这使得即使在无特征区域也能够保持鲁棒性。LDSO保留了这种鲁棒性，同时通过支持跟踪前端的角点特征来确保其中一些点的可重复性。这种可重复使用性允许使用传统的基于特征的词袋（BoW）方法可靠地检测闭环候选。回环检测候选者在几何上被验证，并且通过联合最小化2D和3D几何误差项来估计Sim（3）相对位姿约束。这些约束与从DSO的滑动窗口优化中提取的相对位姿的共同可见性图融合。我们对公开数据集的评估表明，修改后的点选择策略保留了跟踪精度和鲁棒性，集成的姿势图优化显着减少了累积的旋转，平移和尺度漂移，从而使整体性能与最先进的基于特征的系统相当 ，即使没有全局束调整。

  V. CONCLUSION

在本文中，我们提出了一种将回环检测和全局地图优化集成到完全直接视觉里程计系统DSO中的方法。DSO的原始点选择适用于包含可重复的特征。对于那些我们计算ORB描述符并构建用于回环检测的BoW模型。我们证明了点选择保留了视觉里程计前端的原始鲁棒性和准确性，同时使后端能够有效地减少旋转，平移和缩放的全局漂移。我们相信所提出的方法可以扩展到VO或SLAM的未来改进。