Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms

TL;DR

This paper demonstrates that real-time, sub-meter accurate video-based localization is feasible on resource-constrained platforms by using efficient modeling, compression, and feature matching techniques, applicable indoors and outdoors.

Contribution

It introduces novel methods for efficient 3D model building, compression, and feature matching to enable real-time localization on smartphones and embedded devices.

Findings

Achieves sub-meter accuracy in real-time on smartphones

Effective model compression without accuracy loss

Supports indoor and outdoor localization in challenging conditions

Abstract

While the satellite-based Global Positioning System (GPS) is adequate for some outdoor applications, many other applications are held back by its multi-meter positioning errors and poor indoor coverage. In this paper, we study the feasibility of real-time video-based localization on resource-constrained platforms. Before commencing a localization task, a video-based localization system downloads an offline model of a restricted target environment, such as a set of city streets, or an indoor shopping mall. The system is then able to localize the user within the model, using only video as input. To enable such a system to run on resource-constrained embedded systems or smartphones, we (a) propose techniques for efficiently building a 3D model of a surveyed path, through frame selection and efficient feature matching, (b) substantially reduce model size by multiple compression techniques, without sacrificing localization accuracy, (c) propose efficient and concurrent techniques for feature extraction and matching to enable online localization, (d) propose a method with interleaved feature matching and optical flow based tracking to reduce the feature extraction and matching time in online localization. Based on an extensive set of both indoor and outdoor videos, manually annotated with location ground truth, we demonstrate that sub-meter accuracy, at real-time rates, is achievable on smart-phone type platforms, despite challenging video conditions.