Existing visual simultaneous localization and mapping (V-SLAM) algorithms are usually sensitive to the situation with sparse landmarks in the environment and large view transformation of camera motion, and they are liable to generate large pose errors that lead to track failures due to the decrease of the matching rate of feature points. Aiming at the above problems, this article proposes an improved V-SLAM method based on scene segmentation and incremental optimization strategy. In the front end, this article proposes a scene segmentation algorithm considering camera motion direction and angle. By segmenting the trajectory and adding camera motion direction to the tracking thread, an effective prediction model of camera motion in the scene with sparse landmarks and large view transformation is realized. In the back end, this article proposes an incremental optimization method combining segmentation information and an optimization method for tracking prediction model. By incrementally adding the state parameters and reusing the computed results, high-precision results of the camera trajectory and feature points are obtained with satisfactory computing speed. The performance of our algorithm is evaluated by two well-known datasets: TUM RGB-D and NYUDv2 RGB-D. The experimental results demonstrate that our method improves the computational efficiency by 10.2% compared with state-of-the-art V-SLAMs on the desktop platform and by 22.4% on the embedded platform, respectively. Meanwhile, the robustness of our method is better than that of ORB-SLAM2 on the TUM RGB-D dataset.

中文翻译：

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一种基于场景分割和增量优化的视觉同步定位与建图方法

现有的视觉同时定位和映射（V-SLAM）算法通常对环境中地标稀疏和相机运动的大视图变换的情况很敏感，并且它们容易产生大的位姿误差，从而导致跟踪失败。特征点的匹配率。针对上述问题，本文提出了一种基于场景分割和增量优化策略的改进V-SLAM方法。在前端，本文提出了一种考虑相机运动方向和角度的场景分割算法。通过对轨迹进行分割，并在跟踪线程中加入摄像机运动方向，实现了具有稀疏地标和大视图变换的场景中摄像机运动的有效预测模型。在后端，本文提出了一种结合分割信息的增量优化方法和一种跟踪预测模型的优化方法。通过增量添加状态参数并重用计算结果，以令人满意的计算速度获得了相机轨迹和特征点的高精度结果。我们算法的性能由两个著名的数据集评估：TUM RGB-D 和 NYUDv2 RGB-D。实验结果表明，与桌面平台上最先进的 V-SLAM 相比，我们的方法分别将计算效率提高了 10.2%，在嵌入式平台上分别提高了 22.4%。同时，我们的方法在 TUM RGB-D 数据集上的鲁棒性优于 ORB-SLAM2。