Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly- coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual odometry by fusing the velocity of GNSS, pre-integration of Inertial Measurement Unit (IMU) and camera measurement in a tightly-coupled way. The global localization is achieved in the outer-layer, where the local motion is further fused with GNSS position and course in a long-term period in a loosely-coupled way. Furthermore, a dedicated initialization method is proposed to guarantee fast and accurate estimation for all state variables and parameters. We give exhaustive tests of the proposed framework on indoor and outdoor public datasets. The mean localization error is reduced up to 63%, with a promotion of 69% in initialization accuracy compared with state-of-the-art works. We have applied the algorithm to Augmented Reality (AR) navigation, crowd sourcing high-precision map update and other large-scale applications.
翻译:基于实现长期无漂移摄像头的目标,在复杂的假设情景下,我们提出一个全球定位系统框架,将视觉、惯性和全球导航卫星系统(GNSS)的测量分为多层,不同于以往的松散和紧密结合的方法,拟议的多层融合使我们在全球导航卫星系统退化时能够微妙地纠正视觉视像测量的漂移和保持可靠的定位;特别是,在内层进行局部运动估计,通过使用全球导航卫星系统的速度、不高级测量单位(IMU)的预先整合和摄像测量,解决视觉视视离谱学的大规模漂移和不准确的偏差估计问题。全球定位在外层实现,当地运动以松散的混合方式与全球导航卫星系统长期位置和航程进一步结合。此外,还提议了专门的初始化方法,以保障快速和准确估计所有状态变量和参数。我们详尽地测试了拟议的室内和室外公共数据集框架。平均本地化误差正在降低到外部的本地化,而实际的本地化错误正在降低到外部的精确度上层,而实际的市级升级工作则比了69 %。