Abstract：
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.
摘要：
如今,准确和强大的定位是初步实现机器人和新兴应用的高度自治。越来越多,传感器融合以保证这些要求。已经开展了许多相关工作，例如视觉-惯性里程计(VIO)。在这项研究中,受益于IMU和相机的互补感知能力，许多问题已经解决。然而,很少有人关注不同性能IMU对传感器融合精度的影响。当面对实际情况时，特别是在大规模部署硬件的情况下，存在如何适当选择IMU的问题？在本文中，我们选择了六个具有不同性能的具有代表性的IMU，从消费级到战术级进行探索。根据不同场景下基于不同IMU的VIO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,但是在八种实验场景中，不同等级的MEMS-IMU对融合精度的提高不是很明显；消费级IMU也可以获得出色的结果。此外,具有低噪声的IMU在各种场景中更加通用和稳定。研究结果为惯性导航系统(INS)融合的视觉测距技术的发展奠定了基础,为IMU的选择提供指导。
