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arXiv 1808.01111cs.CV
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LDSO：闭环直接稀疏里程计

LDSO: Direct Sparse Odometry with Loop Closure

Technical University of Munich（慕尼黑工业大学）

Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers

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摘要：

在本文中，我们提出了一个扩展的直接稀疏里程（DSO）的单目视觉SLAM系统与环闭合检测和姿态图优化（LDSO）。作为一种直接的技术，DSO可以利用任何具有足够强度梯度的图像像素，这使得它即使在无特征区域也具有鲁棒性。LDSO保留了这种鲁棒性，同时通过支持跟踪前端中的角特征来确保其中一些点的可重复性。这种可重复性允许用传统的基于特征的词袋（BoW）方法可靠地检测循环闭合候选。环闭合候选者被几何地验证，并且通过联合最小化2D和3D几何误差项来估计Sim（3）相对姿态约束。这些约束与从DSO的滑动窗口优化中提取的相对姿态的共视图融合。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，并且集成的姿态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，即使没有全局光束平差。

英文摘要：

In this paper we present an extension of Direct Sparse Odometry (DSO) 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 repeatability 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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