引用本文: 谷晓琳, 杨敏, 张燚, 刘科. 一种基于半直接视觉里程计的RGB-D SLAM算法[J]. 机器人, 2020, 42(1): 39-48.
GU Xiaolin, YANG Min, ZHANG Yi, LIU Ke. An RGB-D SLAM Algorithm Based on Semi-direct Visual Odometry[J]. ROBOT, 2020, 42(1): 39-48.
 Citation: GU Xiaolin, YANG Min, ZHANG Yi, LIU Ke. An RGB-D SLAM Algorithm Based on Semi-direct Visual Odometry[J]. ROBOT, 2020, 42(1): 39-48.

## An RGB-D SLAM Algorithm Based on Semi-direct Visual Odometry

• 摘要: 提出了一种新的基于半直接视觉里程计的RGB-D SLAM（同步定位与地图创建）算法，同时利用直接法和传统特征点法的优势，结合鲁棒的后端优化和闭环检测，有效提高了算法在复杂环境中的定位和建图精度．在定位阶段，采用直接法估计相机的初始位姿，然后通过特征点匹配和最小化重投影误差进一步优化位姿，通过筛选地图点并优化位姿输出策略，使算法能够处理稀疏纹理、光照变化、移动物体等难题．算法具有全局重定位的能力．在后端优化阶段，提出了一种新的关键帧选取策略，同时保留直接法选取的局部关键帧和特征点法选取的全局关键帧，并行地维护2种关键帧，分别在滑动窗口和特征地图中对它们进行优化．算法通过对全局关键帧进行闭环检测和优化，提高SLAM的全局一致性．基于标准数据集和真实场景的实验结果表明，算法的性能在许多实际场景中优于主流的RGB-D SLAM算法，对纹理稀疏和有移动物体干扰的环境的鲁棒性较强．

Abstract: An RGB-D SLAM (simultaneous localization and mapping) algorithm based on semi-direct visual odometry is proposed. By using the advantages of both the direct method and the traditional feature-based method, and combining with robust back-end optimization and closed-loop detection, the localization and mapping accuracy of the algorithm in complex environments is effectively improved. At the localization stage, the initial pose of camera is estimated by the direct method, which is further optimized by matching the features and minimizing the reprojection error. By selecting map points and optimizing the posture output strategy, it can deal with the problems of low-texture, illumination change, moving objects and so on. In addition, the algorithm has the ability of global relocation. A new key frame selection strategy is proposed for back-end optimization, that maintains two separate key frames in parallel, i.e. the local key frames selected by the direct method, and the global key frames selected by the feature-based method. These key frame are optimized in sliding window and feature map respectively. The algorithm improves the global consistency of SLAM through closed-loop detection and optimization on the global key frames. The experimental results based on standard datasets and real scenes show that the performance of the proposed algorithm is better than the main RGB-D SLAM algorithms in many real scenes, and it has strong robustness to the environments with low-texture and moving objects.

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