Optimization-Based Outlier Accommodation for Tightly Coupled RTK-Aided Inertial Navigation Systems in Urban Environments

TL;DR

The paper addresses the problem of GNSS outliers in urban environments by proposing a risk-averse, performance-specified (RAPS) framework integrated with RTK-aided INS for tightly coupled navigation. It extends RAPS to handle carrier-phase measurements via a two-step RTK update, incorporating a non-binary measurement selection and soft constraints to manage infeasible epochs, with an instantaneous RTK strategy to reinitialize ambiguities each epoch. Experimental results on the TEX-CUP deep-urban dataset show that the RAPS-INS-RTK approach yields roughly a 10% improvement over EKF and TD methods and meets SAE-like accuracy requirements (horizontal below 1.5 m, vertical below 3 m for substantial portions of epochs). The work demonstrates smoother trajectories, reduced maximum errors, and practical viability for smartphone-grade sensors, highlighting the value of robust, measurement-selection-based fusion in challenging urban settings.

Abstract

Global Navigation Satellite Systems (GNSS) aided Inertial Navigation System (INS) is a fundamental approach for attaining continuously available absolute vehicle position and full state estimates at high bandwidth. For transportation applications, stated accuracy specifications must be achieved, unless the navigation system can detect when it is violated. In urban environments, GNSS measurements are susceptible to outliers, which motivates the important problem of accommodating outliers while either achieving a performance specification or communicating that it is not feasible. Risk-Averse Performance-Specified (RAPS) is designed to optimally select measurements to address this problem. Existing RAPS approaches lack a method applicable to carrier phase measurements, which have the benefit of measurement errors at the centimeter level along with the challenge of being biased by integer ambiguities. This paper proposes a RAPS framework that combines Real-time Kinematic (RTK) in a tightly coupled INS for urban navigation applications. Experimental results demonstrate the effectiveness of this RAPS-INS-RTK framework, achieving 85.84% and 92.07% of horizontal and vertical errors less than 1.5 meters and 3 meters, respectively, using a smartphone-grade Inertial Measurement Unit (IMU) from a deep-urban dataset. This performance not only surpasses the Society of Automotive Engineers (SAE) requirements, but also shows a 10% improvement compared to traditional methods.

Figures (2)

• Figure 1: The RAPS RTK GNSS/INS workflow.
• Figure 2: Trajectory estimated by TD-INS-RTK with horizontal error magnitude identified by color.