Unsupervised Monocular Visual Odometry Based on Confidence Evaluation
Unsupervised Monocular Visual Odometry Based on Confidence Evaluation
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DOI：
10.1109/tits.2021.3053412
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发表时间：
2021-02-02
影响因子：
8.5
通讯作者：
Wang, Xinlei
中科院分区：
文献类型：
--
作者：
Liu, Yiling;Wang, Hesheng;Wang, Xinlei
随着自动驾驶汽车技术的快速发展，如何在未知复杂的室外环境下进行高精度定位已成为一个重要问题。视觉里程计是一种低成本、应用最广泛的定位方法。传统方法基于多视点几何原理预测相对姿态，对摄像机参数和环境变化敏感。本文研究了基于深度学习的鲁棒性更强的方法。提出了一种基于置信度评估的端到端无监督视觉里程测量框架。其过程可分为两个阶段。首先，通过测量关联图像中几何对应区域的相对相似度生成置信掩模，利用置信掩模预测初始相对姿态变换；第二步是基于轨迹几何一致性评估输出姿态估计的置信度并对其进行细化。在KITTI数据集上对该方法进行了定量和定性评价，证明了该方法在提高姿态估计精度和鲁棒性方面的有效性。
With the rapid development of autonomous vehicle technologies, how to perform high-precision localization in unknown complex outdoor environment has become an important issue. Visual odometry is one of the low-cost and the most widely utilized localization methods. Traditional methods predict relative pose based on the principle of multi-view geometry, which is sensitive to camera parameters and environmental changes. This paper studies deep learning-based methods which can be more robust. A novel end-to-end unsupervised visual odometry framework based on confidence evaluation is proposed. Its process can be divided into two stages. The first is predicting the initial relative pose transformation with the help of confidence mask which is generated by measuring the relative similarity of geometric corresponding regions in associated images. The second is evaluating the confidence of the output pose estimate based on the trajectory geometric consistency and then refining it. Quantitative and qualitative evaluation of the proposed approach on KITTI dataset are presented to demonstrate its effectiveness in improving pose estimation accuracy and robustness.