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HiRo-SLAM: A High-Accuracy and Robust Visual-Inertial SLAM System with Precise Camera Projection Modeling and

Yujuan Deng1,2,3, Liang Tian4, Xiaohui Hou5

  • 1School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

HiRo-SLAM, a novel visual-inertial SLAM system, enhances accuracy and robustness by integrating precise camera modeling, adaptive feature suppression, robust optimization, and point-line feature fusion. This advanced system significantly reduces trajectory errors in challenging environments.

Keywords:
Graduated Non-ConvexityPoint-Line Fusionadaptive feature selectionanalytical Jacobiancamera projection modelrobustnessvisual-inertial SLAM

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Area of Science:

  • Robotics and Computer Vision
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Conventional visual-inertial SLAM methods suffer from biases due to imperfect camera models, uneven feature distribution, and outliers.
  • These limitations hinder accuracy and robustness in real-world applications.

Purpose of the Study:

  • To introduce HiRo-SLAM, a unified optimization framework for high-accuracy and robust visual-inertial SLAM.
  • To address limitations of existing SLAM systems through novel innovations.

Main Methods:

  • Precise Camera Projection Modeling (PCPM) for accurate camera intrinsic and distortion handling.
  • Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) for uniform feature constraints.
  • Robust Optimization Using Graduated Non-Convexity (GNC) to mitigate outlier impact.
  • Point-Line Feature Fusion Frontend combining point and line features for enhanced perception.

Main Results:

  • HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods on EuRoC MAV, TUM-VI, and OIVIO benchmarks.
  • Achieved a 30.0% reduction in absolute trajectory error on the EuRoC MAV dataset.
  • Demonstrated millimeter-level accuracy in controlled conditions.

Conclusions:

  • HiRo-SLAM offers significant improvements in accuracy and robustness for visual-inertial SLAM.
  • The system excels in environments with moderate texture and minimal motion blur.
  • Performance may be limited in highly dynamic scenarios with severe motion blur or extreme lighting.