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A Multi-State Constraint Kalman Filter for Vision-aided ...

A Multi-State Constraint Kalman Filterfor Vision-aided inertial NavigationAnastasios I. Mourikis and Stergios I. RoumeliotisAbstract In this paper, we present an Extended KalmanFilter (EKF)-based algorithm for real-time Vision-aided inertialnavigation. The primary contribution of this work is thederivation of a measurement model that is able to expressthe geometric constraints that arise when a static feature isobserved from multiple camera poses. This measurement modeldoes not require including the 3D feature position in the statevector of the EKF and is optimal, up to linearization Vision-aided inertial navigation algorithm we propose hascomputational complexity onlylinearin the number of features,and is capable of high-precision pose estimation in large-scalereal-world environments.

One family of algorithms for fusing inertial measurements with visual feature observations follows the Simultaneous Localization and Mapping (SLAM) paradigm. In these meth-ods, the current IMU pose, as well as the 3D positions of all visual landmarks are jointly estimated [1]–[4]. These approaches share the same basic principles with SLAM-

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