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Time-Relative RTK-GNSS: GNSS Loop Closure in Pose Graph Optimization (2312.02448v1)

Published 5 Dec 2023 in cs.RO

Abstract: A pose-graph-based optimization technique is widely used to estimate robot poses using various sensor measurements from devices such as laser scanners and cameras. The global navigation satellite system (GNSS) has recently been used to estimate the absolute 3D position of outdoor mobile robots. However, since the accuracy of GNSS single-point positioning is only a few meters, the GNSS is not used for the loop closure of a pose graph. The main purpose of this study is to generate a loop closure of a pose graph using a time-relative real-time kinematic GNSS (TR-RTK-GNSS) technique. The proposed TR-RTK-GNSS technique uses time-differential carrier phase positioning, which is based on carrier-phase-based differential GNSS with a single GNSS receiver. Unlike a conventional RTK-GNSS, we can directly compute the robot's relative position using only a stand-alone GNSS receiver. The initial pose graph is generated from the accumulated velocity computed from GNSS Doppler measurements. To reduce the accumulated error of velocity, we use the TR-RTK-GNSS technique for the loop closure in the graph-based optimization framework. The kinematic positioning tests were performed using an unmanned aerial vehicle to confirm the effectiveness of the proposed technique. From the tests, we can estimate the vehicle's trajectory with approximately 3 cm accuracy using only a stand-alone GNSS receiver.

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Citations (33)
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Summary

  • The paper introduces a TR-RTK-GNSS technique that leverages time-differential carrier phase positioning to eliminate the need for a base station.
  • It employs pose-graph optimization with GNSS Doppler measurements to construct a 3 cm accurate vehicle trajectory.
  • The method simplifies mobile robot localization, enhancing cost efficiency and suggesting new avenues for urban navigation research.

Introduction

The paper presents improvements in using Global Navigation Satellite System (GNSS) technology for estimating the absolute 3D position of outdoor mobile robots such as unmanned aerial vehicles (UAVs). The GNSS is critical for determining global location but has limitations in precision when applied directly due to its meter-level accuracy.

Proposed Technique

The paper introduces a Time-Relative Real-Time Kinematic GNSS (TR-RTK-GNSS) that functions on a single GNSS receiver, relying on time-differential carrier phase positioning. This method stands out by not requiring an additional GNSS base station – a notable difference from conventional RTK-GNSS and its need for communication with a ground base for error correction. Instead, it computes the robot's relative position directly. Using a pose-graph optimization framework combined with GNSS Doppler measurements, the initial graph is formulated. The accumulated velocity error is corrected through TR-RTK-GNSS, enabling loop closure, a key factor in minimizing drift over time.

Evaluation and Results

The implementation of the TR-RTK-GNSS technique was tested with a UAV equipped with a standard GNSS receiver. By constructing a graph using accumulated velocities derived from GNSS Doppler measurements and reducing the accumulated error with the TR-RTK-GNSS technique, the method achieved a vehicle's trajectory estimation with an approximate accuracy of 3 cm. The paper also emphasizes that the technique does not use any other sensors, such as IMUs or odometry systems, relying entirely on GNSS data for its operation.

Implications and Future Work

Given these results, the TR-RTK-GNSS technique promises to significantly improve the cost and efficiency of mobile robotics, such as those used for 3D mapping. The paper suggests that future work may develop methods to handle limitations related to the time difference in the TR-RTK-GNSS technique. Also, expanding the method for usage in urban environments, where the occurrence of GNSS multipath errors is common, is another identified avenue of research. Using robust optimization techniques to tackle multipath errors and potentially extending the current work to support localization of multiple robots are also considerations for future exploration.

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