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Semantic SLAM system for mobile robots based on large visual model in complex environments.

Chao Zheng1, Peng Zhang2, Yanan Li3

  • 1Institute of Physics, Henan Academy of Sciences, Zhengzhou, 450046, Henan, People's Republic of China. arnold_zheng@163.com.

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|March 12, 2025
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Summary
This summary is machine-generated.

This study introduces semantic-based simultaneous localization and mapping (SLAM) for robots in complex environments. This approach enhances robot positioning and mapping accuracy, improving object recognition for advanced tasks.

Keywords:
Computer visionDynamic environmentDynamic point rejectionvisionIntelligent robotSLAMSemantic information

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for unmanned systems like drones and robots.
  • Traditional geometric-feature-based SLAM struggles in complex, dynamic environments, such as hospital wards with mobile personnel and patients.
  • Healthcare robots require accurate positioning and mapping for safe and efficient operation.

Purpose of the Study:

  • To address the limitations of traditional SLAM in complex dynamic environments.
  • To propose a novel semantic-based SLAM method for improved robot localization and mapping.
  • To enhance the robot's ability to identify and interact with surrounding objects.

Main Methods:

  • Development of a method to acquire semantic information of the robot's surroundings.
  • Implementation of a dynamic point culling strategy to optimize data processing.
  • Integration of semantic information into the SLAM framework.

Main Results:

  • The proposed semantic-based SLAM significantly improves localization and mapping accuracy compared to traditional methods.
  • The system demonstrates enhanced capability in identifying surrounding objects within the environment.
  • Experimental validation confirms the effectiveness of the semantic-based approach in complex settings.

Conclusions:

  • Semantic-based SLAM offers superior performance in complex and dynamic environments for robots.
  • The enhanced object recognition capabilities lay the groundwork for more sophisticated robotic tasks.
  • This research advances the application of SLAM in critical domains like healthcare robotics.