Advanced autonomous robotic platforms are increasingly deployed in outdoor environments; these environments can be considered more complex than the traditional office or lab environment, in that they are less structured and allow a wider variety of robot motion. In order for an autonomous system to plan its path through the operating environment, an estimate of its position and orientation is (often) important, if not critical. Pose estimation is the task which deals with estimating the pose, i.e., the position and orientation of a device, based on sensor measurements. Traditional approaches often focus on 2D environments, such as offices, where the device's limited mobility allows for simplification of the required estimation. This work instead focuses on unconstrained six degree of freedom vehicles operating in true 3D environments.The approach developed here decouples the process for estimating a device's orientation from the device's position; this assumes that orientation may be efficiently estimated, provided a sensor such as an IMU. An algorithm is developed that performs orientation estimation efficiently using an extended Kalman filter, while using a particle filter to estimate the position.

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