A novel monocular visual simultaneous localization and mapping (SLAM) algorithm built on the semi-direct method is proposed to deal with some problems in complex environments, such as low-texture, moving objects and perceptual aliasing. The proposed algorithm takes advantage of direct and feature-based methods. On one hand, a direct method is used to track the camera poses and solve the feature alignment. On the other hand, ORB features in keyframes are extracted and matched for optimization and loop closure. To improve the localization accuracy in dynamic environments, a motion detection module that is robust to illumination change is adopted. In addition, for the sake of resolving the loop closure detection problem in perceptual aliasing scenes, this paper fuses the spatial information between two visual words into the bag of visual words (BoVW) model and employs an improved pyramid term frequency-inverse document frequency (TF-IDF) scoring match scheme. Experimental results prove that the proposed algorithm behaves better performance than ORB-SLAM with regard to overall accuracy and speed in complex environments.

提出了一种基于半直接法的单眼视觉同时定位与映射算法，以解决复杂环境下的低纹理，运动物体和感知混叠等问题。所提出的算法利用了直接和基于特征的方法。一方面，直接方法用于跟踪相机的姿势并解决特征对齐问题。另一方面，关键帧中的ORB特征被提取并匹配以进行优化和循环闭合。为了提高动态环境中的定位精度，采用了对光照变化具有鲁棒性的运动检测模块。另外，为了解决感知混叠场景中的闭环检测问题，本文将两个视觉词之间的空间信息融合到视觉词袋（BoVW）模型中，并采用了改进的金字塔词频逆文档频率（TF-IDF）评分匹配方案。实验结果证明，该算法在复杂环境下的整体准确性和速度方面表现出比ORB-SLAM更好的性能。