Abstract: Monocular simultaneous localization and mapping accuracy relies on the feature extraction and association algorithm in image. The estimated trajectory will draft because of the error accumulation. Aiming at the problem, this paper proposes a marker-assisted multi-feature fusion localization algorithm, which combines the environment planar structure where the markers are located to assist localization. The algorithm uses point, marker and plane features to improve the accuracy of pose estimation. Marker feature provides more robust points. Plane feature uses fewer parameters to represent larger structure, reducing the impact of occlusion on feature matching. It establishes the relationships between markers by the planar structures, which can make markers more satisfy the geometric position relationship between each other in the optimization, thereby reducing the drift caused by the accumulated error. Experimental results show that the algorithm can effectively track in the environment containing markers, and can better correct the loop in difficult environments. Compared with similar methods, the accuracy is significantly improved.

Key words: simultaneous localization and mapping, monocular camera, error accumulation, marker feature, plane feature

摘要： 单目同步定位与地图构建的精度依赖于图像中特征的提取与匹配算法，最终估算的轨迹常因其中误差的累积而有所偏移。针对此问题，提出一种标志辅助的多特征融合定位算法，结合标志所处的环境平面结构信息辅助定位。该算法使用点、标志、平面多个特征提高位姿估计的精度，标志特征提供更鲁棒的特征点，面特征以更少的参数表达更大的结构，减少遮挡对特征匹配的影响；且通过环境结构中的平面关系建立标志间关联，使标志在优化中更满足相互之间的几何位置关系，从而减少累积误差造成的漂移。实验结果表明，该算法可以有效地在含有标志的环境中定位相机，且在困难环境中能更好地校正回环，与同类方法相比，精度明显提升。

关键词: 同步定位与建图, 单目相机, 误差累积, 标志特征, 平面特征