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Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking.

Junqiao Zhao1, Yewei Huang2, Xudong He3

  • 1MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China. zhaojunqiao@tongji.edu.cn.

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This study introduces a new semantic landmark-based visual simultaneous localization and mapping (VSLAM) system for autonomous vehicles. It enables robust indoor parking lot localization and creates a human-understandable semantic map.

Keywords:
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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Autonomous Systems

Background:

  • Autonomous driving systems require precise indoor localization for tasks like parking.
  • Current visual simultaneous localization and mapping (VSLAM) systems struggle with featureless environments and poor lighting.
  • Existing VSLAM maps are not easily interpretable by humans.

Purpose of the Study:

  • To develop a robust VSLAM system for real-time autonomous vehicle localization in indoor parking lots.
  • To overcome the limitations of traditional VSLAM in challenging indoor environments.
  • To create a semantic map of parking lots that is usable by both machines and humans.

Main Methods:

  • Proposed a semantic landmark-based VSLAM approach using parking slots as landmarks.
  • Developed a robust optimization framework to handle semantic landmark aliasing.
  • Implemented dynamic elimination of suboptimal constraints and correction of erroneous associations in the pose graph.

Main Results:

  • Achieved robust real-time localization for autonomous vehicles in indoor parking lots.
  • Successfully created a semantic map of the parking lot.
  • Demonstrated a track tracing repeatability of 0.3 meters at 10 kph with multiple autonomous vehicles.

Conclusions:

  • The proposed semantic landmark-based VSLAM system enhances localization accuracy and robustness in indoor parking scenarios.
  • The system generates a semantically rich and human-interpretable map, improving usability.
  • This approach offers a significant advancement for autonomous parking and navigation in complex indoor environments.