Liu Yunfei, Li Zhitian, Zheng Shuaikang, Cai Pengcheng, Zou Xudong
State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Micromachines (Basel). 2022 Apr 12;13(4):602. doi: 10.3390/mi13040602.
Nowadays, accurate and robust localization is preliminary for achieving a high autonomy for robots and emerging applications. More and more, sensors are fused to guarantee these requirements. A lot of related work has been developed, such as visual-inertial odometry (VIO). In this research, benefiting from the complementary sensing capabilities of IMU and cameras, many problems have been solved. However, few of them pay attention to the impact of different performance IMU on the accuracy of sensor fusion. When faced with actual scenarios, especially in the case of massive hardware deployment, there is the question of how to choose an IMU appropriately? In this paper, we chose six representative IMUs with different performances from consumer-grade to tactical grade for exploring. According to the final performance of VIO based on different IMUs in different scenarios, we analyzed the absolute trajectory error of Visual-Inertial Systems (VINS_Fusion). The assistance of IMU can improve the accuracy of multi-sensor fusion, but the improvement of fusion accuracy with different grade MEMS-IMU is not very significant in the eight experimental scenarios; the consumer-grade IMU can also have an excellent result. In addition, the IMU with low noise is more versatile and stable in various scenarios. The results build the route for the development of Inertial Navigation System (INS) fusion with visual odometry and at the same time, provide a guideline for the selection of IMU.
如今，精确且可靠的定位是机器人及新兴应用实现高度自主性的前提。越来越多的传感器被融合以满足这些要求。已经开展了许多相关工作，例如视觉惯性里程计（VIO）。在本研究中，受益于惯性测量单元（IMU）和相机的互补传感能力，许多问题得以解决。然而，其中很少有人关注不同性能的IMU对传感器融合精度的影响。面对实际场景时，尤其是在大规模硬件部署的情况下，如何恰当地选择IMU就成了问题？在本文中，我们选择了六种从消费级到战术级具有不同性能的代表性IMU进行探索。根据基于不同IMU在不同场景下VIO的最终性能，我们分析了视觉惯性系统（VINS_Fusion）的绝对轨迹误差。IMU的辅助可以提高多传感器融合的精度，但在八个实验场景中，不同等级的微机电系统惯性测量单元（MEMS-IMU）对融合精度的提升并不是非常显著；消费级IMU也能有出色的结果。此外，低噪声的IMU在各种场景中更具通用性和稳定性。这些结果为惯性导航系统（INS）与视觉里程计融合的发展构建了路径，同时为IMU的选择提供了指导。
