mlsanityData to 14 Oct 2026

Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision

arXiv 2210.02533 ↗First posted 5 Oct 2022cs.HC cs.CV cs.CYcited by 0

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Abstract (arXiv)

To help improve the safety and accessibility of indoor spaces, researchers and health professionals have created assessment instruments that enable homeowners and trained experts to audit and improve homes. With advances in computer vision, augmented reality (AR), and mobile sensors, new approaches are now possible. We introduce RASSAR (Room Accessibility and Safety Scanning in Augmented Reality), a new proof-of-concept prototype for semi-automatically identifying, categorizing, and localizing indoor accessibility and safety issues using LiDAR + camera data, machine learning, and AR. We present an overview of the current RASSAR prototype and a preliminary evaluation in a single home.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 58 references that are in the map. First posted on arXiv 5 Oct 2022.

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8 of 58 references.

References · 8

  1. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  2. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  3. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  4. 2018BirdNet: a 3D Object Detection Framework from LiDAR informationcited by 33
  5. 2018YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloudcited by 32
  6. 2022Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilitiescited by 3
  7. 2021Strategically using Applied Machine Learning for Accessibility Documentation in the Built Environmentcited by 2
  8. 2021Automatic Mapping with Obstacle Identification for Indoor Human Mobility Assessmentcited by 1

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