# Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision

arXiv: https://arxiv.org/abs/2208.08477 · HTML: https://mlsanity.com/paper/2208.08477/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster](https://mlsanity.com/paper/2112.13194/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction](https://mlsanity.com/paper/2310.20225/) (2023)
- [Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance](https://mlsanity.com/paper/2209.08237/) (2022)
- [A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision](https://mlsanity.com/paper/2505.10875/) (2025)
- [SLAM for Visually Impaired People: a Survey](https://mlsanity.com/paper/2212.04745/) (2022)
- [Robust Computer-Vision based Construction Site Detection for Assistive-Technology Applications](https://mlsanity.com/paper/2503.04139/) (2025)
- [Evaluating OCR Performance for Assistive Technology: Effects of Walking Speed, Camera Placement, and Camera Type](https://mlsanity.com/paper/2602.02223/) (2026)

## Abstract (arXiv)

People with blindness and low vision (pBLV) experience significant challenges when locating final destinations or targeting specific objects in unfamiliar environments. Furthermore, besides initially locating and orienting oneself to a target object, approaching the final target from one's present position is often frustrating and challenging, especially when one drifts away from the initial planned path to avoid obstacles. In this paper, we develop a novel wearable navigation solution to provide real-time guidance for a user to approach a target object of interest efficiently and effectively in unfamiliar environments. Our system contains two key visual computing functions: initial target object localization in 3D and continuous estimation of the user's trajectory, both based on the 2D video captured by a low-cost monocular camera mounted on in front of the chest of the user. These functions enable the system to suggest an initial navigation path, continuously update the path as the user moves, and offer timely recommendation about the correction of the user's path. Our experiments demonstrate that our system is able to operate with an error of less than 0.5 meter both outdoor and indoor. The system is entirely vision-based and does not need other sensors for navigation, and the computation can be run with the Jetson processor in the wearable system to facilitate real-time navigation assistance.