Computationally efficient visual-inertial sensor fusion for GPS-denied navigation on a small quadrotor

Abstract

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide accurate and fast six degree-of-freedom (DOF) state estimation, which is the fundamental requirement for robotic (especially unmanned aerial vehicle) navigation tasks in GPS-denied environments. This paper presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. It is designed to perform 6 DOF state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localization and mapping (mSLAM) algorithm measurement. It also recovers the visual scale for the mSLAM. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, and position fusion is based on a 13-state linear Kalman filter. The elimination of a magnetometer avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the gyroscope and accelerometer are factory calibrated. The resulting algorithm shows a significant computation reduction over the conventional extended Kalman filter with competitive accuracy. Moreover, the separation between the orientation and position fusion process enables the algorithm to be easily implemented into separate hardware, thus allowing the two fusion processes to be executed concurrently

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Identifiers

ORCID for Chang Liu: orcid.org/0000-0002-6967-5159

ORCID for Stephen Prior: orcid.org/0000-0002-4993-4942

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