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Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction.

Yiyi Cai1,2,3, Yang Ou2,3, Tuanfa Qin1,2,3

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.

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|April 13, 2024
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Summary

This study enhances Simultaneous Localization and Mapping (SLAM) by integrating LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and object detection. This improves mapping accuracy in dynamic environments with moving objects.

Keywords:
3D reconstructionSLAMmulti-sensor fusionobject removalstate estimation

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision and Sensor Fusion

Background:

  • Simultaneous Localization and Mapping (SLAM) faces challenges in dynamic environments with variable elements.
  • Robust SLAM requires integrating multiple sensors for reliable performance.

Purpose of the Study:

  • To enhance SLAM robustness and mapping accuracy in complex, dynamic environments.
  • To integrate LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and Inertial Measurement Unit (IMU) preintegration.
  • To incorporate a lightweight object-detection network for real-time transient object exclusion.

Main Methods:

  • Integration of LIO, VIO, and advanced IMU preintegration techniques.
  • Implementation of a lightweight, high-performance object-detection network.
  • Fusion of sensor data and object detection for accurate environmental mapping.

Main Results:

  • Demonstrated improved robustness and reliability of SLAM in environments with variable elements.
  • Achieved precise mapping of complex environments by excluding transient objects.
  • Experimental evaluation confirmed the effectiveness of the proposed integrated approach.

Conclusions:

  • The integrated SLAM approach provides a practical solution for challenging and dynamic settings.
  • Enhanced mapping accuracy and reliability are crucial for autonomous navigation.
  • The study highlights the benefits of sensor fusion and object detection for advanced robotics.