Building a robust implementation of bearing-only inertial SLAM for a UAV

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. (c) 2007 Wiley Periodicals, Inc.