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A survey: which features are required for dynamic visual simultaneous localization and mapping?

Zewen Xu1,2, Zheng Rong1,2, Yihong Wu3,4

  • 1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.

Visual Computing for Industry, Biomedicine, and Art
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

Dynamic simultaneous localization and mapping (SLAM) enhances robot navigation in complex, changing environments by distinguishing static and dynamic features. This survey reviews visual features for improved robotic localization and tracking.

Keywords:
Data associationDynamic simultaneous localization and mappingFeature choicesMultiple objects trackingObject simultaneous localization and mapping

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Standard SLAM assumes static environments, limiting robot applications.
  • Dynamic environments present challenges for traditional SLAM algorithms.
  • Recent advancements focus on dynamic SLAM for real-world robotic deployments.

Purpose of the Study:

  • To survey dynamic SLAM techniques, focusing on the choice of visual features.
  • To analyze the trade-offs of different visual features in dynamic SLAM.
  • To provide insights for robust robot localization in complex, changing environments.

Main Methods:

  • Categorization of environmental features into static and dynamic.
  • Leveraging both static and dynamic features for improved localization accuracy.
  • Integration of dynamic SLAM with multiple object tracking for advanced tasks.

Main Results:

  • Dynamic SLAM offers more robust localization compared to standard SLAM.
  • Feature selection significantly impacts the performance of dynamic SLAM.
  • Discussion on the advantages and disadvantages of various visual features is presented.

Conclusions:

  • Dynamic SLAM is crucial for intelligent robots operating in non-static environments.
  • Careful selection of visual features is key to successful dynamic SLAM implementation.
  • This survey provides a foundation for future research in dynamic SLAM.