Abstract
This chapter focuses on multi-sensor fusion for navigation in difficult environments where none of the existing navigation technologies can satisfy requirements for accurate and reliable navigation if used in a stand-alone mode. A generic multi-sensor fusion approach is presented. This approach builds the navigation mechanization around a self-contained inertial navigator, which is used as a core sensor. Other sensors generally derive navigation-related measurements from external signals, such as Global Navigation Satellite System (GNSS) signals and signals of opportunity (SoOP), or external observations, for example, features extracted from images of laser scanners and video cameras. Depending on a specific navigation mission, these measurements may or may not be available. Therefore, externally-dependent sources of navigation information (including GNSS, SoOP, laser scanners, video cameras, pseudolites, Doppler radars, etc.) are treated as secondary sensors. When available, measurements of a secondary sensor or sensors are utilized to reduce drift in inertial navigation outputs. Inertial data are applied to improve the robustness of secondary sensors’ signal processing. Applications of the multi-sensor fusion approach are illustrated in detail for two case studies: (1) integration of Global Positioning System (GPS), laser scanner, and inertial navigation; and, (2) fusion of laser scanner, video camera, and inertial measurements. Experimental and simulation results are presented to illustrate performance of multi-sensor fusion algorithms.
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Soloviev, A., Miller, M.M. (2012). Navigation in Difficult Environments: Multi-Sensor Fusion Techniques. In: Boginski, V.L., Commander, C.W., Pardalos, P.M., Ye, Y. (eds) Sensors: Theory, Algorithms, and Applications. Springer Optimization and Its Applications(), vol 61. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88619-0_9
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