A robust and accurate simultaneous localization and mapping (SLAM) in working scenarios is an essential competence to perform mobile robotic tasks autonomously. Plenty of research indicates that the extraction of point features from RGB-D data that simultaneously take into account the images and the depth data increases the robustness and precision of the visual odometry method, used either as a self-reliant localization system, or as a front-end in pose-based SLAM. However, due to pure rotation, sudden movements, motion blur, noise and large depth variations, RGB-D SLAM systems often suffer from tracking loss in data association. The front-end tracking process of the ORB-SLAM system requires screening step by step, which is more likely to cause tracking loss. In order to solve the above problems, this work is intended to improve the ORB-SLAM front-end tracking algorithm based on the uniform speed model tracking effective frame and the matching of nearby frame algorithms. Then three datasets selected from TUM datasets with more motion blur are used to further verify the effect of the improved front-end algorithmic architecture. The experimental results suggested that the proposed improved scheme can not only effectively increase the number of tracked frames, but also reduce the amount of computation by about two times under the premise of guaranteeing the path accuracy.

中文翻译：

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RGB-D SLAM前端跟踪算法的精度提高

在工作场景中强大而准确的同时进行本地定位和映射（SLAM）是自主执行移动机器人任务的基本能力。大量研究表明，从RGB-D数据中提取点特征（同时考虑图像和深度数据）可以提高视觉测距法的稳健性和精度，该方法既可以用作自定位系统，也可以用作基于姿势的SLAM中的前端。但是，由于纯旋转，突然运动，运动模糊，噪声和较大的深度变化，RGB-D SLAM系统经常遭受数据关联中的跟踪损失。ORB-SLAM系统的前端跟踪过程需要逐步筛选，这很可能导致跟踪丢失。为了解决上述问题，这项工作旨在基于均匀速度模型跟踪有效帧和附近帧算法的匹配来改进ORB-SLAM前端跟踪算法。然后，从具有更多运动模糊的TUM数据集中选择三个数据集，以进一步验证改进的前端算法体系结构的效果。实验结果表明，提出的改进方案在保证路径精度的前提下，不仅可以有效地增加跟踪帧的数量，而且可以将计算量减少两倍左右。然后，从具有更多运动模糊的TUM数据集中选择三个数据集，以进一步验证改进的前端算法体系结构的效果。实验结果表明，提出的改进方案在保证路径精度的前提下，不仅可以有效地增加跟踪帧的数量，而且可以将计算量减少两倍左右。然后，从具有更多运动模糊的TUM数据集中选择三个数据集，以进一步验证改进的前端算法体系结构的效果。实验结果表明，提出的改进方案在保证路径精度的前提下，不仅可以有效地增加跟踪帧的数量，而且可以将计算量减少两倍左右。