Visual-Inertial Odometry (VIO) usually suffers from drifting over long-time runs, the accuracy is easily affected by dynamic objects. We propose DynaVIG, a navigation and object tracking system based on the integration of Monocular Vision, Inertial Navigation System (INS), and Global Navigation Satellite System (GNSS). Our system aims to provide an accurate global estimation of the navigation states and object poses for the automated ground vehicle (AGV) in dynamic scenes. Due to the scale ambiguity of the object, a prior height model is proposed to initialize the object pose, and the scale is continuously estimated with the aid of GNSS and INS. To precisely track the object with complex moving, we establish an accurate dynamics model according to its motion state. Then the multi-sensor observations are optimized in a unified framework. Experiments on the KITTI dataset demonstrate that the multisensor fusion can effectively improve the accuracy of navigation and object tracking, compared to state-of-the-art methods. In addition, the proposed system achieves good estimation of the objects that change speed or direction.

翻译：视觉内光度测量(VIO)通常会长期漂浮,精确度很容易受到动态物体的影响。我们提议DynavVIG,这是一个基于单视、惯性导航系统和全球导航卫星系统整合的导航和物体跟踪系统。我们的系统旨在提供动态场景中自动地面飞行器导航状态和物体构成的准确的全球估计。由于物体的规模模糊,因此建议先用高度模型来初始化物体表面,并在全球导航卫星系统和INS的帮助下不断对比例进行估计。为了精确跟踪复杂的移动物体,我们根据物体的运动状态建立精确的动态模型。然后在一个统一的框架内优化多传感器观测。对KITTI数据集的实验表明,与最新方法相比,多传感器聚变能够有效地提高导航和物体跟踪的准确性。此外,拟议的系统还可以对改变速度或方向的物体进行良好的估计。