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首页> 外文期刊>IEEE transactions on industrial informatics >An SfM Algorithm With Good Convergence That Addresses Outliers for Realizing Mono-SLAM
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An SfM Algorithm With Good Convergence That Addresses Outliers for Realizing Mono-SLAM

机译：具有良好收敛性的SfM算法，用于解决实现Mono-SLAM的异常值

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摘要

Monocular simultaneous localization and mapping (mono-SLAM) is a key component of autonomous robot visual navigation. Recently, the structure from motion (SfM) approach has become an attractive means of implementing mono-SLAM because of its high accuracy; although, in this application, the SfM approach must be operable in real time and robust to outliers. However, because of strong nonlinearity, conventional SfM methods, such as bundle adjustment, must consider multiple initial values to obtain the globally optimal result, which is time consuming. In this paper, a novel iterative SfM algorithm based on the object-space objective function is proposed. To improve the method’s robustness to outliers and to incorporate the information obtained from other types of sensors, an approach to closely integrate the proposed SfM algorithm with extended Kalman filter-based SLAM is proposed. Experimental results using both synthesized and real data are consistent with our theory, and verify that the main advantage of the proposed SfM algorithm is its good convergence. The algorithm is therefore particularly appropriate for realizing mono-SLAM, because when rotations can be obtained approximately using gyroscope information, the algorithm is globally convergent from any initial value.
机译：单眼同时定位和制图（mono-SLAM）是自主机器人视觉导航的关键组成部分。最近，基于运动的结构（SfM）方法由于其高精度而成为一种有吸引力的实现单SLAM的方法;尽管在此应用中，SfM方法必须实时且对异常值具有鲁棒性。但是，由于强烈的非线性，传统的SfM方法（例如束调整）必须考虑多个初始值才能获得全局最优结果，这很耗时。提出了一种新的基于目标空间目标函数的迭代SfM算法。为了提高该方法对异常值的鲁棒性并整合从其他类型的传感器获得的信息，提出了一种将建议的SfM算法与基于扩展卡尔曼滤波器的SLAM紧密集成的方法。使用合成数据和真实数据进行的实验结果均与我们的理论相符，并证明了所提出的SfM算法的主要优势是其良好的收敛性。因此，该算法特别适合于实现单SLAM，因为当可以使用陀螺仪信息近似获得旋转时，该算法可以从任何初始值全局收敛。

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