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.

翻译：视觉里程计与同步定位与地图构建（SLAM）作为计算机视觉与机器人领域的重要研究任务，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方法中，运动视觉传感器从不同视角观测三维点集，通常通过特征追踪与匹配建立各图像中二维投影点间的对应关系。然而，由于对应点可能存在噪声与误差，可靠的不确定性估计能提升里程计/SLAM方法的精度。此外，惯性测量单元常被用于辅助视觉传感器实现视觉-惯性融合。本文提出一种基于惯性引导的特征对应不确定性估计方法，该方法对运动模糊、光照变化及遮挡导致的图像退化具有鲁棒性。通过构建引导分布以采样可能的对应关系，我们将该分布拟合至基于图像误差的能量函数，从而获得比传统方法更稳健的不确定性估计。进一步地，通过将本方法集成至近期视觉-惯性里程计/SLAM算法中，在公开数据集上验证了其可行性。