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Related Experiment Video

Updated: Apr 15, 2026

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures
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Navigation Error Characteristics of LIO-, VIO-, and RIMU-Assisted INS/GNSS Multi-Sensor Fusion Schemes in a

Kai-Wei Chiang1, Syun Tsai1, Chi-Hsin Huang1

  • 1Department of Geomatics, National Cheng Kung University, Tainan City 701, Taiwan.

Sensors (Basel, Switzerland)
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Summary

Autonomous vehicles need accurate navigation without satellite signals. This study fuses LiDAR and cameras with redundant inertial sensors, achieving lane-level accuracy for over 7 minutes in GNSS-denied conditions.

Keywords:
LiDAR inertial odometry (LIO)boresight angle calibrationinertial measurement unit (IMU)integrated navigation systemredundant inertial measurement unit (RIMU)visual inertial odometry (VIO)

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Area of Science:

  • Robotics
  • Navigation Systems
  • Sensor Fusion

Background:

  • Autonomous vehicles require precise localization, especially in Global Navigation Satellite System (GNSS)-denied environments.
  • Micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) suffer from drift during extended GNSS outages.

Purpose of the Study:

  • To enhance autonomous vehicle navigation accuracy in GNSS-denied scenarios.
  • To mitigate IMU drift by integrating Visual Inertial Odometry (VIO) and LiDAR Inertial Odometry (LIO).
  • To improve sensor reliability and reduce noise using a Redundant IMU (RIMU) approach.

Main Methods:

  • Integration of VIO and LIO as external updates for IMU-based navigation.
  • Fusion of multiple low-cost IMUs in a RIMU configuration.
  • System calibration using static and dynamic vehicle motion for extrinsic parameter estimation.
  • Experimental validation in a 7-minute GNSS-denied underground parking scenario.

Main Results:

  • The proposed INS/GNSS/LIO framework achieved a 2D root-mean-square position error of 1.22 m, meeting lane-level accuracy (1.5 m) for over 7 minutes without prior maps.
  • The INS/GNSS/VIO framework resulted in a 4.71 m 2D mean position error under identical conditions.
  • The system calibration achieved a boresight angle root-mean-square error of 0.04 degrees in simulation.

Conclusions:

  • The study demonstrates the effectiveness of fusing LiDAR Inertial Odometry with GNSS and IMU for robust autonomous navigation in GNSS-denied environments.
  • Redundant IMU configurations enhance sensor reliability and reduce noise.
  • The proposed methods provide a quantitative comparison for VIO-, LIO-, and RIMU-assisted INS/GNSS fusion strategies.