首页|平面环境约束辅助的单目视觉惯性里程计

平面环境约束辅助的单目视觉惯性里程计

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为提高视觉惯性导航系统在宽动态、 长航时、 大范围作业环境中的精确性与鲁棒性,提出了一种平面环境约束辅助的单目视觉惯性里程计.通过在视频图像中提取并跟踪均匀分布的FAST特征点,并采用对称光流剔除误跟踪点,实现了视觉特征点高效检测与精确跟踪;无需计算稠密深度地图,仅从稀疏特征点集中检测共面特征点,拟合空间平面,构建了对视觉特征点三维坐标的空间几何约束;融合视觉特征点重投影误差、 共面特征点坐标约束以及惯性测量单元(Inertial measurement unit,IMU)预积分误差构造代价函数,采用非线性优化方法估计了系统状态.最后,分别在公开数据集和实际户外场景中评估了算法的准确性和有效性.实验结果表明,相较于VINS-Mono和ORB-SLAM3算法,本文方法的定位结果有显著提升,实现了复杂应用环境中精确、 稳定导航,在机器人、 无人驾驶等领域具有较大的实用价值.
Planar environmental constraints aided monocular visual inertial odometry
Motivated by the goal of enhancing the accuracy and robustness of visual inertial navigation systems(VINSs) across a wide spectrum of dynamic scenarios, protracted missions and expansive navigation ranges, we designed a monocular visual inertial odometry (VIO) augmented by planar environmental constraints. To attain efficient feature extraction and precise feature tracking, we employed a methodology that involved the extraction and tracking of uniformly distributed using features from accelerated segment test(FAST) feature points from video images, with the subsequent removal of outliers through symmetric optical flow. Additionally, we outlined the process of identifying coplanar feature points from the sparse feature set, enabling efficient plane detection and fitting. This approach constructed spatial geometric constraints on the three-dimensional coordinates of visual feature points without resorting to computationally expensive dense depth mapping. The heart of this method lied in the formulation of a comprehensive cost function, which integrated the reprojection error of visual feature points, the coordinate constraints derived from coplanar feature points, and the inertial measurement unit (IMU) pre-integration error. These integrated measurements were then utilized to estimate the system states through a nonlinear optimization methodology. To validate the accuracy and effectiveness of the proposed approach, extensive experiments were conducted using publicly available datasets and large-scale outdoor scenes. The experimental results conclusively demonstrate that compared to VINS-Mono and ORB-SLAM3, the proposed method achieves higher positioning accuracy. It can deliver precise and stable navigation results even in challenging conditions, thereby imparting significant practical value to the fields of robotics and unmanned driving.

visual inertial odometry(VIO)planar environmental constraintstate estimationnonlinear optimization

多靖赟、赵毅琳、赵龙、李俊韬

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北京物资学院智能物流系统北京市重点实验室,北京 101149

北京航空航天大学自动化科学与电气工程学院,北京 100191

视觉惯性里程计 平面环境约束 状态估计 非线性优化

Beijing Tongzhou District Science and Technology Innovation Talent FoundationNational Science Foundation of ChinaAeronautical Science Foundation of ChinaBeijing Wuzi University Youth Research Foundation

JCQN2023030422740372022Z0220510012022XJQN22

2024

10.62756/jmsi.1674-8042.2024009

测试科学与仪器
中北大学

测试科学与仪器

影响因子：0.111
ISSN：1674-8042
年,卷(期)：2024.15(1)
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