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60 unmanned systems inside April/May 2016 GROUND AUTOMOTIVE Reconfi gurable Integration Filter (RIFE) Figure 4 is a block diagram of RIFE, which consists of three major components, namely, (i) sensor measurement abstraction, (ii) generic filter error state observable generation, and (iii) filter reconfiguration, together with configura- tion management. As opposed to most existing integrated naviga- tion solutions that are tailored for specific sensor configurations, RIFE is mechanized as an object- oriented multi-sensor estimation, which truly enables plug-and-play navigation functionality independent of any specific sensor set. Various sensors are represented by generic classes in the RIFE library. Each class is designed for a generic type of sensors (rather than for a specific sensor) wherein sensor types are defined by the type of their measurements. When a sensor is connected to the system, RIFE is reconfigured by identifying the sensor's measurement type and instantiating a sensor object according to the corresponding class from the RIFE library without need for redesign. When a sensor is disconnected from the system, its object is removed to free computer resources. RIFE utilizes a generic self-contained dead reckoning navigator (DRN) as its core sensor. For the results presented in this paper, an iner- tial navigation system (INS) provides the dead- reckoning navigation solution. However, RIFE can use other types of dead-reckoning (e.g., an odometer/yaw rate sensor and a motion model) for those scenarios where a complete six degree- of-freedom (6DOF) inertial measurement suite is not available. The DRN does not rely on any type of exter- nal information and as a result can operate in any environment. However, a dead-reckoning solution drifts over time. To mitigate DRN drift, this core sensor is augmented with reference navigation data sources. Reference data sources generally rely on external observations or signals, which may not be always available. Therefore, these sources are treated as secondary sensors to provide aiding observables whenever possible to reduce drift in DRN outputs. Figure 4: Reconf gurable integration f ltering engine (RIFE) Figure 5: Examples of Relative Position Observables Figure 6: Plug-and-play functionality of RIFE ODOMETER POSITION CHANGE FROM 2D LIDAR PLANAR SURFACES EXTRACTED FROM CONSECUTIVE 3D LIDAR IMAGES Position change projected onto forward axis Position change projected onto x and y axes of the body-frame Position change projected onto the plane normal vector