InCrowd-VI: A Dataset for Indoor Navigation in Crowded Spaces
Author Information
Author(s): Bamdad Marziyeh, Hutter Hans-Peter, Darvishy Alireza, Lázaro-Galilea José Luis
Primary Institution: Institute of Computer Science, Zurich University of Applied Sciences
Hypothesis
The lack of realistic datasets limits the development of robust SLAM solutions for navigating crowded indoor spaces.
Conclusion
The InCrowd-VI dataset reveals significant performance limitations in state-of-the-art SLAM algorithms when tested in realistic crowded scenarios.
Supporting Evidence
- The dataset includes 58 sequences with a total trajectory length of 4998.17 m and a recording time of 1 h, 26 min, and 37 s.
- Ground-truth trajectories are accurate to approximately 2 cm.
- State-of-the-art SLAM algorithms showed severe performance limitations in crowded scenarios.
- Deep learning-based approaches maintained high pose estimation coverage but failed to achieve real-time processing speeds.
- The dataset captures challenges such as pedestrian occlusions, varying crowd densities, and complex layouts.
Takeaway
This study created a new dataset to help robots and systems navigate crowded indoor spaces, especially for people who can't see well. It shows that current technology struggles in these busy environments.
Methodology
The dataset was collected using Meta Aria Project glasses in various indoor environments, capturing RGB images, stereo images, and IMU measurements.
Limitations
The dataset lacks depth information and focuses solely on indoor environments, limiting its applicability for outdoor navigation.
Participant Demographics
The dataset captures realistic human motion patterns from visually impaired individuals navigating crowded spaces.
Digital Object Identifier (DOI)
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