NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired People

TL;DR

NaVIP introduces an infrastructure-free, image-based indoor navigation system for visually impaired people, leveraging a large-scale labeled dataset to enable scalable, real-time assistance without costly hardware or environment modifications.

Contribution

The paper presents a novel image-centric indoor navigation approach for VIPs, including a large-scale dataset with precise labels, and benchmarks for positioning and exploration support.

Findings

Achieved scalable real-time positioning accuracy.

Demonstrated effective exploration support for VIPs.

Provided publicly available dataset and tools for further research.

Abstract

Indoor navigation is challenging due to the absence of satellite positioning. This challenge is manifold greater for Visually Impaired People (VIPs) who lack the ability to get information from wayfinding signage. Other sensor signals (e.g., Bluetooth and LiDAR) can be used to create turn-by-turn navigation solutions with position updates for users. Unfortunately, these solutions require tags to be installed all around the environment or the use of fairly expensive hardware. Moreover, these solutions require a high degree of manual involvement that raises costs, thus hampering scalability. We propose an image dataset and associated image-centric solution called NaVIP towards visual intelligence that is infrastructure-free and task-scalable, and can assist VIPs in understanding their surroundings. Specifically, we start by curating large-scale phone camera data in a four-floor research building, with 300K images, to lay the foundation for creating an image-centric indoor navigation and exploration solution for inclusiveness. Every image is labelled with precise 6DoF camera poses, details of indoor PoIs, and descriptive captions to assist VIPs. We benchmark on two main aspects: 1) positioning system and 2) exploration support, prioritizing training scalability and real-time inference, to validate the prospect of image-based solution towards indoor navigation. The dataset, code, and model checkpoints are made publicly available at https://github.com/junfish/VIP_Navi.