On Data Sharing Strategy for Decentralized Collaborative Visual-Inertial Simultaneous Localization And Mapping

Abstract

This article introduces and evaluates two decentralized data sharing algorithms for multi-robot visual-inertial simultaneous localization and mapping (VI-SLAM): Factor Sparsification for Visual-Inertial Packets (FS-VIP) and Min-K-Cover Selection for Visual-Inertial Packets (MKCS-VIP). Both methods make robots regularly build and exchange data packets which describe the successive portions of their map, but rely on distinct paradigms. While FS-VIP builds on consistent marginalization and sparsification techniques, MKCSVIP selects raw visual and inertial information which can best help to perform a faithful and consistent re-estimation while reducing the communication cost. Performances in terms of accuracy and communication loads are evaluated on multi-robot scenarios built on both available (EUROC) and custom datasets (SOTTEVILLE).