This article proposes a novel synthetic aperture radar visual-inertial odometry (SAR-VIO) consisting of an SAR and an inertial measurement unit (IMU), which aims to enable the observation platform to complete successfully a continuous observation mission in the context of low-cost demand and lack of enough navigation information. First, we establish the observation models of the SAR in a continuous observation process based on the SAR frequency-domain imaging algorithm and the SAR time-domain imaging algorithm, respectively. With the preintegrated IMU data, we then propose a method for estimating the geographic locations of the matched targets in the SAR images and verify the condition and correctness of the method. The optimization of the track and the locations of the targets is achieved by bundle adjustment according to the minimum reprojection error criterion, and a sparse point-cloud map can be obtained. Finally, these methods and models are organized into a complete SAR-VIO framework, and the feasibility of the framework is verified through experiments.

中文翻译：

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SAR视觉惯性里程表的首次尝试

本文提出了一种由SAR和惯性测量单元（IMU）组成的新型合成孔径雷达视觉惯性里程表（SAR-VIO），其目的是使观测平台能够在低辐射情况下成功完成连续观测任务。成本需求和导航信息不足。首先，我们分别基于SAR频域成像算法和SAR时域成像算法建立了连续观测过程中SAR的观测模型。利用预先集成的IMU数据，我们提出了一种用于估计SAR图像中匹配目标的地理位置的方法，并验证了该方法的条件和正确性。通过根据最小重投影误差准则对光束进行调整，可以实现对目标轨迹和位置的优化，可以获得稀疏的点云图。最后，将这些方法和模型组织成一个完整的SAR-VIO框架，并通过实验验证了该框架的可行性。