摘要：

在弱纹理环境中，当前的单目视觉惯性同步定位与地图构建(SLAM)存在视觉退化和误差偏移的问题，导致系统位姿估计精度不高。为解决此问题，提出一种基于特征协同的单目视觉惯性SLAM方法，首先对惯性测量单元(IMU)数据进行预积分并联合视觉信息进行松耦合的初始化, 获取系统的先验信息和尺度信息，再引入线特征提取算法并对提取出的线特征进行优化，以减小计算开销。基于点线特征的位置关系和几何特性，使用特征协同关联算法在点特征和线特征之间建立稳定的关联约束，从而提升点特征跟踪的可靠性。提出一种基于多源信息融合的联合代价函数优化方法，对点特征重投影误差、线特征重投影误差以及IMU残差进行优化以提升位姿估计精度。在EuRoc和TUM Ⅵ公共数据集以及真实环境中的实验结果表明，相较于主流的视觉惯性SLAM方法，本文方法的在线特征检测和跟踪耗时平均减少26.5%，位姿估计均方根误差平均降低38.6%和43%，由此验证本文方法在弱纹理环境下具有更高的位姿估计精度。

关键词: 同步定位与地图构建, 视觉惯性, 视觉退化, 特征协同, 多源信息

Abstract:

In weak-texture environments, the current monocular visual-inertial Simultaneous Localization and Mapping (SLAM) suffers from visual degradation and error drift, leading to decreased accuracy in pose estimation. To address this issue, a monocular visual-inertial SLAM method is proposed based on feature collaboration. Initially, the Inertial Measurement Unit (IMU) data is pre-integrated, and a loosely coupled initialization with visual information is performed to obtain prior information and scale information of the system. Subsequently, a line feature extraction algorithm is introduced to optimize extracted line features, therefore reducing computational overhead. Based on positional relationships and geometric characteristics of point and line features, a feature collaborative association algorithm is employed to establish stable association constraints between point and line features, thereby enhancing the reliability of point feature tracking. Finally, a joint cost function optimization method based on multi-source information fusion is introduced to optimize point feature reprojection errors, line feature reprojection errors, and IMU residuals, resulting in improved pose estimation accuracy. Experimental results on the EuRoc and TUM Ⅵ public datasets, as well as in real environments, demonstrate that compared to mainstream visual-inertial SLAM methods, the proposed method reduces the average time consumption of line feature detection and tracking by 26.5%. Additionally, the root mean square error of pose estimation is reduced by an average of 38.6% and 43%. These findings validate that the proposed method achieves superior pose estimation accuracy in weak-texture environments.

Key words: Simultaneous Localization and Mapping (SLAM), visual-inertial, visual degradation, feature collaboration, multi-source information