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A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for

Bo You1,2, Guangjin Zhong1, Chen Chen1,2

  • 1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China.

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|July 14, 2023
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Summary
This summary is machine-generated.

This study introduces a robust Simultaneous Localization and Mapping (SLAM) system for autonomous robots, improving outdoor localization accuracy by integrating LiDAR, IMU, and filtered GPS data, especially during GPS signal loss.

Keywords:
Global Positioning System (GPS)Iterative Error State Kalman Filter (IESKF)LiDARSimultaneous Localization and Mapping (SLAM)autonomous mobile robotsinertial measurement unit (IMU)

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Outdoor autonomous mobile robots depend on GPS for localization, but urban environments and signal loss cause significant deviations.
  • Existing SLAM systems struggle with GPS inaccuracies and signal interruptions, limiting reliable outdoor navigation.
  • Accurate localization is critical for safe and efficient autonomous robot operation in diverse environments.

Purpose of the Study:

  • To develop an improved SLAM system addressing GPS inaccuracies and signal loss for outdoor autonomous robots.
  • To enhance localization accuracy and robustness in challenging environments with unreliable GPS data.
  • To provide a reliable navigation solution for autonomous mobile robots operating in urban and GPS-denied areas.

Main Methods:

  • Proposed a SLAM system combining Iterated Extended Kalman Filter (IESKF) and factor graph optimization.
  • Utilized LiDAR and Inertial Measurement Unit (IMU) data for front-end pose estimation via IESKF.
  • Implemented a GPS signal filtering method based on state and confidence, and integrated processed GPS, IMU, and loop closure factors into a back-end factor graph.

Main Results:

  • The proposed SLAM system demonstrated superior localization accuracy compared to FAST-LIO and LIO-SAM on the KITTI dataset and a custom platform.
  • Significant performance improvements were observed, particularly in scenarios with intermittent or lost GPS signals.
  • The integration of filtered GPS data and sensor fusion enhanced the overall robustness of the localization system.

Conclusions:

  • The developed SLAM system effectively overcomes GPS limitations, offering enhanced localization accuracy and reliability for outdoor autonomous robots.
  • The proposed sensor fusion and GPS filtering approach provides a robust solution for navigation in challenging environments.
  • This work contributes to advancing autonomous navigation capabilities in real-world applications where GPS is often unreliable.