Department of Geomatics, National Cheng-Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan.
Sensors (Basel). 2010;10(10):9252-85. doi: 10.3390/s101009252. Epub 2010 Oct 15.
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks--the Cascade Correlation Neural Network (CCNNs)--to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.
移动测绘系统已广泛应用于空间信息系统和三维城市模型等领域，用于获取空间信息。目前，用于移动测绘系统定位和定向的最常见技术包括全球定位系统 (GPS) 作为主要定位传感器和惯性导航系统 (INS) 作为主要定向传感器。在经典方法中，卡尔曼滤波器 (KF) 方法的局限性和多传感器系统的总体价格限制了大多数基于地面的移动测绘应用的普及。尽管为了提高低成本微机电系统 (MEMS) INS/GPS 集成系统的性能，已经提出了由多层前馈神经网络 (MFNN) 组成的智能传感器定位和定向方案，这是最著名的人工神经网络 (ANN) 之一，以及 KF/平滑器，但 MFNN 的自动化应用并不像最初预期的那样容易。因此，本研究不仅解决了在以前研究中提出的 INS/GPS 集成系统的 MFNN-KF/平滑器算法中应用的传统方法中自动化程度不足的问题，而且还利用和分析了以更自动的方式集成各种传感器的替代智能传感器定位和定向方案的想法。所提出的方案是使用最著名的构造性神经网络之一——级联相关神经网络 (CCNNs) 来实现的，以克服基于 KF/平滑器算法的传统技术以及先前开发的 MFNN-平滑器方案的局限性。与 MFNN 相比，所应用的 CCNN 还具有更灵活的拓扑结构的优势。基于所使用的实验数据，本文提出的初步结果说明了与平滑器算法以及 MFNN-平滑器方案相比，所提出方案的有效性。
