A Cooperative Augmented Reality Framework Grounded on Distributive Visual Simultaneous Localization and Mapping

Abstract

Tesfaye Adisu and Abaysew Ayele

Distributive Simultaneous Localizations and Mapping (SLAM) helps for multiple agents for exploring and building a global map predicting their locations. The challenge is difficult to identify local map overlaps these agents, especially when their initial relative positions are unknown. So, to address this problem, a collaborative (AR) framework with liberally moving agents were used without know how of their initial comparative positions. Each agent in the framework used a camera only as the input device for its SLAM route.

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