Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data -- despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1-2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar.

翻译：精确定位是机器人导航系统的核心组成部分。为此，全球导航卫星系统（GNSS）可在户外提供绝对测量值，从而消除长期漂移。然而，将GNSS数据与其他传感器数据进行融合并非易事，尤其是当机器人在有天空视野和无天空视野的区域之间移动时。我们提出了一种鲁棒方法，该方法将原始GNSS接收器数据与惯性测量及可选的激光雷达观测进行紧融合，以实现精确且平滑的移动机器人定位。我们提出了包含两种GNSS因子的因子图：第一类是基于伪距的因子，可实现全球范围内的绝对定位；第二类是基于载波相位的因子，可实现高精度的相对定位，这在其他传感模态受挑战时尤为有用。与传统差分GNSS不同，本方法无需连接基站。在一个公开的城市驾驶数据集上，我们的方法达到了与融合视觉惯性里程计与GNSS数据的最先进算法相当的精度——尽管本方法未使用相机，仅依赖惯性和GNSS数据。我们还利用从汽车和四足机器人在天空可见度较低的环境（如森林）中采集的数据，展示了本方法的鲁棒性。在全球地球坐标系中的精度仍为1-2米，且估计的轨迹无间断且平滑。我们还展示了如何紧集成激光雷达测量值。我们相信这是首个将原始GNSS观测值（而非定位解）与激光雷达进行融合的系统。