| 基于深度特征的单目视觉惯导里程计 |
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| 引用本文： | 徐伟锋,蔡述庭,熊晓明. 基于深度特征的单目视觉惯导里程计[J]. 广东工业大学学报, 2023, 40(1): 56-60,76. DOI: 10.12052/gdutxb.210028 |
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| 作者姓名： | 徐伟锋 蔡述庭 熊晓明 |
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| 作者单位： | 广东工业大学 自动化学院，广东 广州 510006 |
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| 基金项目： | 广东省应用型科技研发专项(2017B090909004) |
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| 摘 要： | 视觉里程计是SLAM (Simultaneous Localization and Mapping)领域中的基石，单目视觉里程计因其成本低廉和仅需较少的相机标定工作而占据着重要的地位，但它存在着尺度不确定、尺度漂移、鲁棒性差等缺点。本文在ORB＿SLAM3的基础上，提出了一种基于深度特征的单目视觉惯导里程计，简称DF-VIO(Visual Inertial Odometry Based on Deep Features)，它采用深度学习网络提取的深度特征替代传统的人工点特征，并融合了人工线特征，强化了系统在现实复杂场景下的鲁棒性；另外，系统提供了多种位姿跟踪方式，除了基于恒速模型和跟踪参考关键帧的方式外，还提供了一种基于深度学习网络的可重复性图的位姿跟踪方法，进一步提高了系统位姿跟踪的精度。在公开数据集Eu Ro C上进行对比实验，在纯视觉模式下，平均轨迹误差下降了25.9%，在视觉惯导模式下，平均轨迹误差下降了8.6%，证明了本文提出的系统在复杂的场景下能够具有更高的鲁棒性。
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| 关 键 词： | 视觉里程计 深度学习 惯导 线特征 |
| 收稿时间： | 2021-02-18 |
Visual Inertial Odometry Based on Deep Features |
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| Xu Wei-feng,Cai Shu-ting,Xiong Xiao-ming. Visual Inertial Odometry Based on Deep Features[J]. Journal of Guangdong University of Technology, 2023, 40(1): 56-60,76. DOI: 10.12052/gdutxb.210028 |
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| Authors: | Xu Wei-feng Cai Shu-ting Xiong Xiao-ming |
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| Affiliation: | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract: | Visual odometry is the cornerstone in the field of SLAM. Monocular visual odometry occupies an important position because of its low cost and less camera calibration, but it has some shortcomings such as scale uncertainty, scale drift, poor robustness, and so on. To solve these problems, based on ORB-SLAM3, we process a monocular visual-inertial navigation odometer with depth features, referred to as DF-VIO (Visual Inertial Odometry Based on Deep Features) , which uses depth features extracted by deep learning network to replace traditional artificial point features, and fuses artificial line features to enhance the robustness of the system in real complex scenes. Besides, the system provides a variety of pose tracking methods. In addition to the method based on the constant speed model and tracking reference keyframe, a pose tracking method based on the predicted repeatability map is also provided, which further improves the accuracy of system pose tracking. Comparative experiments are carried out on the open data set EuRoC, and the average trajectory error is reduced by 25.9% in pure visual mode and 8.6% in visual-inertial mode, which proves that the system proposed in this paper can be more robust in complex scenes. |
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| Keywords: | visual inertial odometry deep learning inertial measurement unit line features |
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