Monocular visual odometry (VO) has extensive applications in mobile robots and computer vision. However, current applications of monocular VO systems in complex environments still have limitations. Accurate, robust, and easy-to-use VO is still an unsolved problem to some extent. In recent years, the single-chip millimeter-wave (mmWave) radar has been increasingly used in various types of mobile robots due to its advantages of small size, low cost, and robustness in harsh weather conditions. In this article, we apply the mmWave radar to a VO system and propose a multistage visual-radar fusion odometry framework, MS-VRO. The framework is based on a typical monocular VO system. By merging mmWave radar data in different stages, the proposed odometry improves the accuracy, robustness, and generalization ability of VO. The framework contains a new visual-radar initialization method, a visual-radar joint optimization method, and a radar-aided visual feature selection and processing method that can remove dynamic object features and bad map points. Through these, the proposed method solves the problems of monocular VO, including scale ambiguity, scale drift, and performance degradation in dynamic environments. We build a dataset that can be used for research on visual-radar fusion odometry and test the proposed method on the new dataset and other public datasets. The result shows that the proposed odometry achieves significantly better performance than VO methods and is more accurate and robust compared to some typical visual-inertial odometry methods.

中文翻译：

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MS-VRO：多级视觉毫米波雷达融合里程计


单目视觉里程计（VO）在移动机器人和计算机视觉领域有着广泛的应用。然而，目前单目VO系统在复杂环境中的应用仍然存在局限性。准确、鲁棒且易于使用的 VO 在某种程度上仍然是一个未解决的问题。近年来，单芯片毫米波（mmWave）雷达凭借其体积小、成本低、恶劣天气条件下稳健等优点，越来越多地应用于各类移动机器人中。在本文中，我们将毫米波雷达应用于 VO 系统，并提出了一种多级视觉雷达融合里程计框架 MS-VRO。该框架基于典型的单目 VO 系统。通过合并不同阶段的毫米波雷达数据，所提出的里程计提高了 VO 的准确性、鲁棒性和泛化能力。该框架包含新的视觉雷达初始化方法、视觉雷达联合优化方法以及可以去除动态目标特征和不良地图点的雷达辅助视觉特征选择和处理方法。通过这些，所提出的方法解决了单目 VO 的问题，包括动态环境中的尺度模糊、尺度漂移和性能下降。我们构建了一个可用于视觉雷达融合里程计研究的数据集，并在新数据集和其他公共数据集上测试了所提出的方法。结果表明，所提出的里程计比 VO 方法具有明显更好的性能，并且与一些典型的视觉惯性里程计方法相比更加准确和鲁棒。