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

arXiv: https://arxiv.org/abs/2210.02533 · HTML: https://mlsanity.com/paper/2210.02533/

## 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.

## Builds on (most cited first, 8 of 8)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://mlsanity.com/paper/2009.00784/) (2020)
- [BirdNet: a 3D Object Detection Framework from LiDAR information](https://mlsanity.com/paper/1805.01195/) (2018)
- [YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud](https://mlsanity.com/paper/1808.02350/) (2018)
- [Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities](https://mlsanity.com/paper/2206.13677/) (2022)
- [Strategically using Applied Machine Learning for Accessibility Documentation in the Built Environment](https://mlsanity.com/paper/2107.14506/) (2021)
- [Automatic Mapping with Obstacle Identification for Indoor Human Mobility Assessment](https://mlsanity.com/paper/2111.12690/) (2021)

## 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.