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 in a factor graph.

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）可在室外提供绝对测量值，从而消除长期漂移。然而，将GNSS数据与其他传感器数据融合并非易事，特别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，将原始GNSS接收器数据与惯性测量值（以及可选的激光雷达观测数据）进行紧耦合，以实现精确且平滑的移动机器人定位。该方法采用包含两类GNSS因子的因子图：第一类基于伪距的因子，可实现地球上的全球定位；第二类基于载波相位的因子，能够实现高精度的相对定位，在其它传感模式受限时尤为有用。与传统差分GNSS不同，本方法无需连接基站。在公开的城市驾驶数据集上，我们的方法在未使用相机（仅使用惯性和GNSS数据）的情况下，达到了与融合视觉惯性里程计与GNSS数据的最新算法相当的精度。我们还通过汽车和四足机器人在森林等低天空可见度环境中的运动数据验证了该方法的鲁棒性。在全球地球坐标系下的精度仍可达1-2米，且估计轨迹无间断且平滑。我们进一步展示了如何实现激光雷达测量值的紧耦合。据我们所知，这是首个在因子图中融合原始GNSS观测数据（而非定位解）与激光雷达的系统。