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Integrated Bionic Polarized Vision/VINS for Goal-Directed Navigation and Homing in Unmanned Ground Vehicle
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2021-03-17 , DOI: 10.1109/jsen.2021.3066844
Wenzhou Zhou , Chen Fan , Xiaofeng He , Xiaoping Hu , Ying Fan , Xuesong Wu , Hang Shang

In this paper we present a bionic multi-sensor anavigation and control system for unmanned ground vehicles to complete the homing task. The system consists of the pixelated polarized sensor, a Micro Inertial Measurement Unit (MIMU), and a monocular camera. To compensate for the installation error, we provide a joint calibration method for multiple sensors. Utilizing the measurements of pixelated polarized vision sensor, we firstly propose an adaptive integrated method with the Visual-Inertial System. The integrated algorithm can not only solve the ambiguity problem of polarized orientation and reduce the cumulative error of the system, but also increase the navigation output rate and enhance the robustness of the system. We present a homevector-based strategy for the goal-directed navigation and control of the unmanned ground vehicles. When the external data link is interrupted, the unmanned vehicles can return to the starting point autonomously, which benefits for improving the survivability of the system. Finally, we design various experiments to verify the algorithm proposed in this paper. The experimental results of the calibration demonstrate that the RMSE of the orientation after calibration is only 0.014°. In the navigation and homing experiment, the RMSE of the position error is 0.64m, and the minimum homing error is only 0.49m (1.09% of the travelled distance). Finally, we discuss interesting insights gained with respect to future work in multi-sensor integration and robot control strategies.

中文翻译:

集成的仿生偏光视觉/ VINS,可用于无人地面车辆的目标定向导航和制导

在本文中,我们提出了一种用于无人地面车辆的仿生多传感器导航和控制系统,以完成制导任务。该系统由像素偏振传感器,微惯性测量单元(MIMU)和单眼相机组成。为了补偿安装错误,我们提供了针对多个传感器的联合校准方法。利用像素化偏振视觉传感器的测量,我们首先提出了一种与视觉惯性系统集成的自适应方法。该集成算法不仅解决了极化方向的歧义性问题,减少了系统的累积误差,而且提高了导航输出速率,增强了系统的鲁棒性。我们提出了一种基于Homevector的策略,用于无人地面车辆的目标定向导航和控制。当外部数据链路中断时,无人驾驶汽车可以自动返回起点,这有利于提高系统的生存能力。最后,我们设计了各种实验来验证本文提出的算法。校准的实验结果表明,校准后取向的RMSE仅为0.014°。在导航和归位实验中,位置误差的RMSE为0.64m,最小归位误差仅为0.49m(行程的1.09%)。最后,我们讨论了有关未来在多传感器集成和机器人控制策略中的工作所获得的有趣见解。我们设计了各种实验来验证本文提出的算法。校准的实验结果表明,校准后取向的RMSE仅为0.014°。在导航和归位实验中,位置误差的RMSE为0.64m,最小归位误差仅为0.49m(行程的1.09%)。最后,我们讨论了有关未来在多传感器集成和机器人控制策略中的工作所获得的有趣见解。我们设计了各种实验来验证本文提出的算法。校准的实验结果表明,校准后取向的RMSE仅为0.014°。在导航和归位实验中,位置误差的RMSE为0.64m,最小归位误差仅为0.49m(行程的1.09%)。最后,我们讨论了有关未来在多传感器集成和机器人控制策略中的工作所获得的有趣见解。
更新日期:2021-04-20
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