视觉惯性里程计/SLAM中特征匹配的惯性引导不确定性估计
Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM
AI总结：
针对视觉惯性里程计/SLAM中特征匹配易受图像退化影响的问题，提出惯性引导的特征匹配不确定性估计方法，通过构建引导分布拟合图像误差能量函数提升鲁棒性，并在公开数据集上验证了有效性。
摘要：
视觉里程计与同步定位与建图（Simultaneous Localization And Mapping，SLAM）是计算机视觉与机器人领域最重要的研究任务之一，可为自主导航和增强现实系统提供支撑。在基于特征的里程计/SLAM中，运动的视觉传感器从不同视角观测一组三维点，各图像中投影二维点之间的匹配通常通过特征跟踪与匹配建立。但由于匹配点可能存在错误与噪声，可靠的不确定性估计能够提升里程计/SLAM方法的精度。此外，在视觉惯性融合框架中，惯性测量单元被用于辅助视觉传感器。本文提出一种利用惯性引导估计特征匹配不确定性的方法，该方法对运动模糊、光照变化和遮挡导致的图像退化具有鲁棒性。我们通过构建引导分布来采样可能的匹配结果，将该分布拟合至基于图像误差的能量函数，相比传统方法得到更鲁棒的不确定性估计。我们还将该方法集成到一种最新的视觉惯性里程计/SLAM算法中，在公开数据集上验证了方法的可行性。
英文摘要：
Visual odometry and Simultaneous Localization And Mapping (SLAM) has been studied as one of the most important tasks in the areas of computer vision and robotics, to contribute to autonomous navigation and augmented reality systems. In case of feature-based odometry/SLAM, a moving visual sensor observes a set of 3D points from different viewpoints, correspondences between the projected 2D points in each image are usually established by feature tracking and matching. However, since the corresponding point could be erroneous and noisy, reliable uncertainty estimation can improve the accuracy of odometry/SLAM methods. In addition, inertial measurement unit is utilized to aid the visual sensor in terms of Visual-Inertial fusion. In this paper, we propose a method to estimate the uncertainty of feature correspondence using an inertial guidance robust to image degradation caused by motion blur, illumination change and occlusion. Modeling a guidance distribution to sample possible correspondence, we fit the distribution to an energy function based on image error, yielding more robust uncertainty than conventional methods. We also demonstrate the feasibility of our approach by incorporating it into one of recent visual-inertial odometry/SLAM algorithms for public datasets.
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