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 strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors. This module loosely fuses up-to-scale VO and ranging data using either a quadratically constrained quadratic programming (QCQP) or nonlinear least squares (NLS) algorithm based on whether a good initial guess is available. Secondly, we provide an accompanied theoretical analysis that includes the derivation and interpretation of the Fisher Information Matrix (FIM) and its determinant. Thirdly, we present UWBaided bundle adjustment (UBA) and UWB-aided pose graph optimization (UPGO) modules to improve short-term odometry accuracy, reduce long-term drift as well as correct any alignment and scale errors. Extensive simulations and experiments show that our solution outperforms UWB/camera-only and previous approaches, can quickly recover from tracking failure without relying on visual relocalization, and can effortlessly obtain a global map even if there are no loop closures.

翻译：本文提出了一种基于单目相机和超宽带传感器的同步定位与建图系统。该系统被称为VRSLAM，是一个多阶段框架，能够充分发挥各传感器的优势并弥补其不足。首先，我们引入了一种超宽带辅助的7自由度（尺度因子、三维位置和三维方向）全局对齐模块，用于在超宽带锚点定义的世界坐标系中初始化视觉里程计系统。该模块基于是否具有良好初始猜测，通过二次约束二次规划或非线性最小二乘算法对尺度未定的视觉里程计和测距数据进行松耦合融合。其次，我们提供了相应的理论分析，包括Fisher信息矩阵及其行列式的推导与解释。第三，我们提出了超宽带辅助光束法平差和超宽带辅助位姿图优化模块，以提高短期里程计精度、减少长期漂移，并修正对齐与尺度误差。大量仿真与实验表明，我们的解决方案优于纯超宽带/纯相机方法及先前方法，能够在无需依赖视觉重定位的情况下快速从跟踪失败中恢复，并且即使没有回环也能轻松获取全局地图。