Two stage GNSS outlier detection for factor graph optimization based GNSS-RTK/INS/odometer fusion

2025-10-01

AI Summary

In complex urban environments, GNSS pseudorange measurements suffer from severe outliers due to non-line-of-sight (NLOS) propagation and multipath effects, degrading the performance of RTK and tightly-coupled GNSS/INS/odometry systems. To address this, we propose a two-stage outlier detection method: (1) coarse screening using Doppler observations, and (2) fine-grained identification and rejection of residual outliers via double-differenced pseudorange prediction, leveraging pre-integrated IMU motion models and odometry constraints. The method is embedded within a factor graph optimization framework, jointly incorporating Doppler consistency checks, IMU-derived motion priors, and odometry-aided pseudorange prediction. Experimental evaluation in urban canyon scenarios demonstrates that the proposed approach reduces positioning RMSE from 0.52 m to 0.30 m—a 42.3% improvement—significantly enhancing system robustness and reliability.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Reliable GNSS positioning in complex environments remains a critical challenge due to non-line-of-sight (NLOS) propagation, multipath effects, and frequent signal blockages. These effects can easily introduce large outliers into the raw pseudo-range measurements, which significantly degrade the performance of global navigation satellite system (GNSS) real-time kinematic (RTK) positioning and limit the effectiveness of tightly coupled GNSS-based integrated navigation system. To address this issue, we propose a two-stage outlier detection method and apply the method in a tightly coupled GNSS-RTK, inertial navigation system (INS), and odometer integration based on factor graph optimization (FGO). In the first stage, Doppler measurements are employed to detect pseudo-range outliers in a GNSS-only manner, since Doppler is less sensitive to multipath and NLOS effects compared with pseudo-range, making it a more stable reference for detecting sudden inconsistencies. In the second stage, pre-integrated inertial measurement units (IMU) and odometer constraints are used to generate predicted double-difference pseudo-range measurements, which enable a more refined identification and rejection of remaining outliers. By combining these two complementary stages, the system achieves improved robustness against both gross pseudo-range errors and degraded satellite measuring quality. The experimental results demonstrate that the two-stage detection framework significantly reduces the impact of pseudo-range outliers, and leads to improved positioning accuracy and consistency compared with representative baseline approaches. In the deep urban canyon test, the outlier mitigation method has limits the RMSE of GNSS-RTK/INS/odometer fusion from 0.52 m to 0.30 m, with 42.3% improvement.

Problem

• Detecting GNSS outliers in complex environments with NLOS and multipath effects
• Improving GNSS-RTK/INS/odometer fusion robustness against measurement errors
• Enhancing positioning accuracy through two-stage outlier detection method

Innovation

• Two-stage outlier detection for GNSS-RTK/INS/odometer fusion
• First stage uses Doppler measurements for GNSS-only detection
• Second stage employs IMU and odometer constraints for refinement