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Abstract This paper presents the on‐going design and implementation of a robust inertial sensor based simultaneous localization and mapping (SLAM) algorithm for an unmanned aerial vehicle (UAV) using bearing‐only observations. A single color vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete six degrees‐of‐freedom motion of the UAV along with the three‐dimensional position of the features in the environment. An extended Kalman filter approach is used where a technique of delayed initialization is performed to initialize the three‐dimensional position of features from bearing‐only observations. Data association is achieved using a multihypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off‐line using inertial sensor and vision data collected during a flight test of a UAV. © 2007 Wiley Periodicals, Inc.
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