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InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating Simultaneous Localization and Mapping in Indoor

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New InCrowd-VI dataset aids visually impaired navigation by testing Simultaneous Localization and Mapping (SLAM) in crowded indoor spaces. Current SLAM systems struggle with real-world challenges, highlighting the need for better algorithms.

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Area of Science:

  • Robotics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for visually impaired navigation.
  • Existing SLAM datasets lack realism for crowded indoor environments.
  • Development of robust SLAM for pedestrian-rich spaces is hindered by data limitations.

Purpose of the Study:

  • Introduce InCrowd-VI, a novel visual-inertial dataset for human navigation in indoor, pedestrian-rich environments.
  • Provide a realistic benchmark for evaluating SLAM and visual odometry (VO) algorithms.
  • Address the limitations of current datasets in capturing complex navigation challenges.

Main Methods:

  • Recorded 58 sequences (5 km, 1.5 h) using Meta Aria Project glasses, capturing RGB, stereo images, and IMU data.
  • Dataset includes realistic challenges: pedestrian occlusions, varying crowd densities, complex layouts, and lighting changes.
  • Provided ground-truth trajectories (~2 cm accuracy) and semi-dense 3D point clouds for each sequence.

Main Results:

  • State-of-the-art VO and SLAM algorithms showed severe performance limitations on InCrowd-VI.
  • Systems exceeded localization accuracy (0.5 m) and drift thresholds (1%) in challenging conditions.
  • Classical methods drifted 5-10%, while deep learning approaches lacked real-time processing speeds.

Conclusions:

  • InCrowd-VI dataset reveals significant performance gaps in current SLAM algorithms for visually impaired navigation.
  • The dataset is essential for advancing SLAM research in complex indoor environments.
  • Further development is needed for real-time, accurate SLAM systems capable of handling real-world navigation challenges.