Abstract
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.
Abstract (translated)
在本研究中,我们提出了一种利用单目相机和超宽带(UWB)传感器同时定位和绘图的系统,我们称之为VRSLAM。我们的系统被称为VRSLAM,它是一个多阶段框架,利用每个传感器的优势并补偿其劣势。首先,我们引入了一个UWB辅助的7自由度(尺度、三维位置和三维取向)全球对齐模块,以在由UWB锚点定义的世界框架中初始化视觉导航(VO)系统。这个模块松散地结合到Scale-aware VO和距离数据,根据是否存在良好的初始猜测,使用quadratically constrained quadratic programming(QCQP)或非线性最小二乘法(NLS)算法进行非线性最小平方优化。其次,我们提供了伴随的理论分析,包括费舍尔信息矩阵(FIM)的推导和解释以及其决定值的阐述。第三,我们介绍了UWB辅助分组调整(UBA)和UWB辅助姿态图优化(UPGO)模块,以提高短期导航精度、减少长期漂移并纠正任何对齐和尺度错误。广泛的模拟和实验表明,我们的解决方案比仅使用UWB和相机的方法出色,能够迅速从跟踪失败中恢复,无需依赖视觉重定向,并且即使不存在循环终点,也能轻松获得全球地图。
URL
https://arxiv.org/abs/2303.10903
