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Accurate localization is a core component of a robot's navigation system. Tothis end, global navigation satellite systems (GNSS) can provide absolutemeasurements outdoors and, therefore, eliminate long-term drift. However,fusing GNSS data with other sensor data is not trivial, especially when a robotmoves between areas with and without sky view. We propose a robust approachthat tightly fuses raw GNSS receiver data with inertial measurements and,optionally, lidar observations for precise and smooth mobile robotlocalization. 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 relativelocalization, which is useful when other sensing modalities are challenged.Unlike traditional differential GNSS, this approach does not require aconnection to a base station. On a public urban driving dataset, our approachachieves accuracy comparable to a state-of-the-art algorithm that fuses visualinertial odometry with GNSS data -- despite our approach not using the camera,just inertial and GNSS data. We also demonstrate the robustness of our approachusing data from a car and a quadruped robot moving in environments with littlesky visibility, such as a forest. The accuracy in the global Earth frame isstill 1-2 m, while the estimated trajectories are discontinuity-free andsmooth. We also show how lidar measurements can be tightly integrated. Webelieve this is the first system that fuses raw GNSS observations (as opposedto fixes) with lidar in a factor graph.

Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station