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In this work, we propose a simultaneous localization and mapping (SLAM)system using a monocular camera and Ultra-wideband (UWB) sensors. Our system,referred to as VRSLAM, is a multi-stage framework that leverages the strengthsand compensates for the weaknesses of each sensor. Firstly, we introduce aUWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation)global alignment module to initialize the visual odometry (VO) system in theworld frame defined by the UWB anchors. This module loosely fuses up-to-scaleVO and ranging data using either a quadratically constrained quadraticprogramming (QCQP) or nonlinear least squares (NLS) algorithm based on whethera good initial guess is available. Secondly, we provide an accompaniedtheoretical analysis that includes the derivation and interpretation of theFisher Information Matrix (FIM) and its determinant. Thirdly, we presentUWBaided bundle adjustment (UBA) and UWB-aided pose graph optimization (UPGO)modules to improve short-term odometry accuracy, reduce long-term drift as wellas correct any alignment and scale errors. Extensive simulations andexperiments show that our solution outperforms UWB/camera-only and previousapproaches, can quickly recover from tracking failure without relying on visualrelocalization, and can effortlessly obtain a global map even if there are noloop closures.

VR-SLAM: A Visual-Range Simultaneous Localization and Mapping System using Monocular Camera and Ultra-wideband Sensors