Zhong Min, Yao Yiqing, Xu Xiaosu, Wei Hongyu
Key Laboratory of Micro-Inertial Instruments and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2022 Oct 29;22(21):8307. doi: 10.3390/s22218307.
In order to improve the initialization robustness of visual inertial SLAM, the complementarity of the optical flow method and the feature-based method can be used in vision data processing. The parallel initialization method is proposed, where the optical flow inertial initialization and the monocular feature-based initialization are carried out at the same time. After the initializations, the state estimation results are jointly optimized by bundle adjustment. The proposed method retains more mapping information, and correspondingly is more adaptable to the initialization scene. It is found that the initialization map constructed by the proposed method features a comparable accuracy to the one constructed by ORB-SLAM3 in monocular inertial mode. Since the online extrinsic parameter estimation can be realized by the proposed method, it is considered better than ORB-SLAM3 in the aspect of portability. By the experiments performed on the benchmark dataset EuRoC, the effectiveness and robustness of the proposed method are validated.
为了提高视觉惯性同步定位与地图构建（SLAM）的初始化鲁棒性，可在视觉数据处理中利用光流法和基于特征的方法的互补性。提出了并行初始化方法，即同时进行光流惯性初始化和基于单目特征的初始化。初始化后，通过光束平差对状态估计结果进行联合优化。该方法保留了更多的地图信息，相应地对初始化场景的适应性更强。研究发现，该方法构建的初始化地图在单目惯性模式下与ORB-SLAM3构建的地图具有相当的精度。由于该方法可实现在线外部参数估计，因此在便携性方面被认为优于ORB-SLAM3。通过在基准数据集EuRoC上进行的实验，验证了该方法的有效性和鲁棒性。
