This paper 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

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... 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 details 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.