Reliable GNSS Joint Position and Attitude Estimation in Harsh Environments through Robust Statistics

Next-generation navigation systems require precise and robust solutions, providing information about both the system position and its attitude, of particular interest in intelligent transportation systems and robotics applications. Within this context, Global Navigation Satellite Systems (GNSS) are the main source of positioning data and, in multiple antenna setups, can also provide attitude information. Notice that the use of phase observables is mandatory to obtain a precise solution. In this contribution, we leverage the recently introduced recursive GNSS joint position and attitude (JPA) estimation framework, which has been shown to provide good performance under nominal conditions. The main goal is to further elaborate the JPA problem and to propose a new robust filtering solution able to mitigate the impact of possible outliers, which may otherwise cause a performance breakdown of standard JPA, because of the sensitivity of carrier phase measurements. Illustrative results are provided to support the discussion and show the performance improvement of the proposed approach

Robust M-Type Error-State Kalman Filters for Attitude Estimation

State estimation techniques appear in a plethora of engineering fields. Both standard Kalman filter (KF) and its nonlinear extensions, as well as particle filters, consider a known system model (i.e., functions and noise statistics), an assumption which may not hold in practice. A problem of particular interest is how to deal with outliers in the observation model. A possible solution is to resort to the framework of robust statistics, where a robust score function is used to mitigate the impact of outlying measurements, leading to robust M-type KFs. In this contribution, some of these robust filtering results are extended to the case where states may live on a manifold (unit norm quaternion), and propose robust iterated error-state M-type KF solutions. An illustrative example is provided to show the performance of the proposed filter and support the discussion

Array PPP-RTK: A High Precision Pose Estimation Method for Outdoor Scenarios

Advanced driver-assistance system (ADAS) and high levels of autonomy for vehicular applications require reliable and high precision pose information for their functioning. Pose estimation comprises solving the localization and orientation problem for a rigid body in a three-dimensional space. In outdoor scenarios, the fusion of Global Navigation Satellite Systems (GNSS) and inertial data in high-end receivers constitutes the baseline for ground truth localization solutions, such as Real-Time Kinematic (RTK) or Precise Point Positioning (PPP). These techniques present two main disadvantages, namely the inability to provide absolute orientation information and the lack of observations redundancy in urban scenarios. This paper presents Array PPP-RTK, a recursive three-dimensional pose estimation technique which fuses inertial and multi-antenna GNSS measurements to provide centimeters and sub-degree precision for positioning and attitude estimates, respectively. The core filter is based on adapting the well-known Extended Kalman Filter (EKF), such that it deals with parameters belonging to the SO(3) and GNSS integer ambiguity groups. The Array PPP-RTK observation model is also introduced, based on the combination of carrier phase measurements over multiple antennas along with State Space Representation (SSR) GNSS corrections. The performance assessment is based on the real data collected on an inland waterway scenario. The results demonstrate that a high precision solution is available 99.5% of the time, with a horizontal precision of around 6 cm and heading precision of 0.9 degrees. Despite the satellite occlusion after bridge passing, it is shown that Array PPP-RTK recovers high accurate estimates in less than ten seconds