GNSS Odometry: Precise Trajectory Estimation Based on Carrier Phase Cycle Slip Estimation
(2312.02424)Abstract
This paper proposes a highly accurate trajectory estimation method for outdoor mobile robots using global navigation satellite system (GNSS) time differences of carrier phase (TDCP) measurements. By using GNSS TDCP, the relative 3D position can be estimated with millimeter precision. However, when a phenomenon called cycle slip occurs, wherein the carrier phase measurement jumps and becomes discontinuous, it is impossible to accurately estimate the relative position using TDCP. Although previous studies have eliminated the effect of cycle slip using a robust optimization technique, it was difficult to completely eliminate the effect of outliers. In this paper, we propose a method to detect GNSS carrier phase cycle slip, estimate the amount of cycle slip, and modify the observed TDCP to calculate the relative position using the factor graph optimization framework. The estimated relative position acts as a loop closure in graph optimization and contributes to the reduction in the integration error of the relative position. Experiments with an unmanned aerial vehicle showed that by modifying the cycle slip using the proposed method, the vehicle trajectory could be estimated with an accuracy of 5 to 30 cm using only a single GNSS receiver, without using any other external data or sensors.
Overview
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The paper presents a method to enhance trajectory estimation in outdoor mobile robots using GNSS by addressing cycle slip in carrier phase measurements.
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A graph optimization framework is used to detect, estimate, and correct cycle slips, thus improving position estimation accuracy.
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The method introduces a factor graph that includes cycle slip amounts and TDCP factors to achieve accurate relative position estimation.
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Experiments demonstrate that this approach surpasses other techniques in correcting cycle slips and maintaining precise estimation.
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The research highlights the potential for better outdoor robotic applications and identifies future research opportunities in NLOS multipath correction.
Overview
The discussed paper introduces a method for improving the trajectory estimation of outdoor mobile robots using the Global Navigation Satellite System (GNSS), specifically through handling the issue of cycle slip in carrier phase measurements. Cycle slip refers to the sudden losses of the continuous carrier phase tracking, which harm the accuracy of trajectory estimations derived from time differences in GNSS carrier phase (TDCP) measurements. This paper's method detects, estimates, and corrects cycle slip within a graph optimization framework, showing significant improvements in position estimation accuracy.
Methodology
The research presents a tightly coupled integration approach that uses TDCP measurements directly as constraints for graph optimization, enhancing the trajectory estimation process. A novel factor graph structure is proposed that incorporates the estimated amount of cycle slip. Through this structure, TDCP factors that account for cycle slip are used to modify and maintain accurate relative position estimation. The method also incorporates constraints on the time variation of cycle slip in the graph, adjusting for errors based on the likelihood of cycle slip occurrence.
Experimental Validation
The paper details experiments using an unmanned aerial vehicle (UAV) and static GNSS receivers, showcasing that the proposed method can accurately correct for cycle slips. Even in scenarios with extended time intervals between measurements, the approach manages to maintain a precise relative position estimation. Comparative analyses in the experiments demonstrate that the new method outperforms other existing techniques, including robust optimization techniques and the authors' previous work, known as Time-Relative RTK-GNSS.
Implications and Future Work
The experimental results suggest that by incorporating the proposed strategy for handling cycle slips in carrier phase measurements, it is possible to improve trajectory estimations significantly. Such improvements have practical implications for various applications of outdoor robots, such as mapping, delivery, and unmanned operations. Additionally, the paper recognizes the challenge of applying the approach in urban environments, where low-elevation satellites are often obstructed, and proposes addressing non-line-of-sight (NLOS) multipath as an area for future research.
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