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Enhancing RTK Performance in Urban Environments by Tightly Integrating INS and LiDAR

Xingxing Li,Shiwen Wang,Shengyu Li,Yuxuan Zhou,Chunxi Xia,Zhiheng Shen

Published 2023 in IEEE Transactions on Vehicular Technology

ABSTRACT

High-precision and continuous positioning is a fundamental requirement for intelligent navigation applications. Nowadays, the global navigation satellite system (GNSS) real-time kinematic (RTK) technique is recognized as a feasible solution to provide precise positioning services, but its accuracy is susceptible to signal attenuation and will deteriorate drastically in urban environments. Fortunately, the low-cost inertial measurement units (IMU) and light detection and ranging (LiDAR) are available in modern vehicle systems and could be integrated to enhance GNSS performance. In this contribution, aiming to improve RTK performance in GNSS-challenging environments, we propose a tightly coupled RTK/Inertial navigation system (INS)/LiDAR integration method. The GNSS double-differenced pseudorange and carrier-phase measurements, IMU data, and LiDAR plane features are fused at the raw-data level via an extended Kalman filter (EKF). Both simulated tests and real-world experiments were conducted to evaluate the effectiveness of the proposed method. The results indicate that the proposed method is able to achieve sub-decimeter-level accuracy in GNSS-challenging environments, with the improvements of (51.8%, 82.0%, 75.0%) and (53.9%, 71.0%, 41.5%) compared to state-of-the-art LIO-SAM and loosely coupled GNSS/INS/LiDAR integration. Meanwhile, the ambiguity fixing rate could be improved by more than 10% with the assistance of LiDAR plane features. Similar improvements can also be achieved in velocity and attitude estimation.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    IEEE Transactions on Vehicular Technology

  • Publication date

    2023-08-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TVT.2023.3257874
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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