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Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using

Yangwoo Lee1, Minsoo Kim1, Joonwoo Ahn2

  • 1Dynamic Robotic Systems (DYROS) Lab, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.

Sensors (Basel, Switzerland)
|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces a robust Around View Monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) system for autonomous parking. The novel approach corrects for AVM distortion errors, significantly improving vehicle localization accuracy.

Keywords:
AVM distortion errorautonomous parkingdeep learningvisual Simultaneous Localization and Mappingweighted Generalized Iterative Closest Point

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Accurate vehicle pose estimation is crucial for autonomous parking systems.
  • Around View Monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) is suitable for parking due to its cost-effectiveness and suitability for dynamic movements.
  • AVM distortion errors from inaccurate camera calibration degrade real-world SLAM performance.

Purpose of the Study:

  • To develop an AVM-based visual SLAM method robust to AVM distortion errors for autonomous parking.
  • To improve the reliability and accuracy of vehicle localization in challenging parking environments.

Main Methods:

  • A deep learning network was developed to weight parking line features based on AVM distortion.
  • Three-dimensional (3D) Light Detection and Ranging (LiDAR) data and parking lot guidelines were used for training data generation.
  • The trained network's output was integrated into a weighted Generalized Iterative Closest Point (GICP) algorithm for localization.

Main Results:

  • The proposed method demonstrated robustness against AVM distortion errors.
  • Localization errors were reduced by an average of 39% compared to existing AVM-based visual SLAM approaches.
  • The system effectively handles rapid vehicle rotations and back-and-forth movements common in parking scenarios.

Conclusions:

  • The presented AVM-based visual SLAM system offers improved accuracy and robustness for autonomous parking.
  • The deep learning-based weighting mechanism effectively mitigates the impact of AVM distortion errors.
  • This approach enhances the feasibility of reliable autonomous parking in real-world conditions.