This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.

本文提出了一种新颖的7自由度（即定向，平移和缩放）可视化同时定位和制图（vSLAM）系统，用于室外环境中的移动机器人。在此vSLAM系统的前端，针对不同的vSLAM骨干网设计了一种快速初始化方法，该方法使用从构建块生成的深度图计算出的绝对比例来提高vSLAM的轨迹和重构精度。在此vSLAM的后端，我们提出了一种非线性优化机制，通过该机制可以将多峰数据组合在一起以实现更强大的优化。优化中的构建模块的形式可以提高跟踪精度和规模估计。通过整合从视觉信息估计的姿态和通过GPS接收的位置，优化可以进一步减轻漂移。