Center for Spatial Information Science (CSIS), University of Tokyo, Chiba 277-8568, Japan.
Sensors (Basel). 2012 Dec 21;13(1):119-36. doi: 10.3390/s130100119.
Accurate localization of moving sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous sensor model in a panoramic camera. The rigorous model does not cause system errors, thus representing an improvement over the widely used ideal sensor model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional sensors, such as expensive inertial measurement units. In this paper, the problems of the ideal sensor model for a panoramic camera are analysed, and a rigorous sensor model is established. GPS data are then introduced for global optimization and georeferencing. Using the rigorous sensor model with the geometric observation equations of BA, a GPS-supported BA-SLAM approach that combines ray observations and GPS observations is then established. Finally, our method is applied to a set of vehicle-borne panoramic images captured from a campus environment, and several ground control points (GCP) are used to check the localization accuracy. The results demonstrated that our method can reach an accuracy of several centimetres.
移动传感器的精确定位对于机器人导航和城市测绘等许多领域都至关重要。在本文中，我们提出了一种基于 GPS 的全景相机视觉同时定位与地图构建（BA-SLAM）框架，该框架使用严格的传感器模型。严格的模型不会引起系统误差，因此优于广泛使用的理想传感器模型。所提出的 SLAM 不需要额外的限制，例如闭环，也不需要额外的传感器，例如昂贵的惯性测量单元。本文分析了全景相机理想传感器模型存在的问题，并建立了严格的传感器模型。然后引入 GPS 数据进行全局优化和地理参考。利用 BA 的几何观测方程和严格的传感器模型，建立了一种结合光线观测和 GPS 观测的 GPS 支持的 BA-SLAM 方法。最后，我们将该方法应用于一组从校园环境中采集的车载全景图像，并使用了几个地面控制点（GCP）来检查定位精度。结果表明，我们的方法可以达到几厘米的精度。
