Loop Closing in a Drift-Aware Monocular SLAM

Abstract : This paper presents a real-time Consistent Mono-cular EKF-SLAM process. We introduce the notion of bias which allows to model the natural drift of the SLAM process. Thanks to it , the consistency of the filter is guaranteed. By connecting the bias to the different landmarks and to the vehicle pose , the estimates become tightly bound to the SLAM drift. It means that a loop closure , for instance , will naturally estimate the bias and so correct the vehicle pose and landmark positions without any special processing. We developed a dedicated architecture in order to integrate the bias. It uses an Extended Kalman Filter and has the advantage to be totally decorrelated from the classical SLAM process. Thanks to it , any algorithm , with any kind of sensors or methods , can be used instead of the monocular SLAM employed in this paper , as long as it produces landmark estimates and their uncertainty. This approach was validated with a real experiment composed of a long loop .
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Guillaume Bresson, Romuald Aufrère, Roland Chapuis. Loop Closing in a Drift-Aware Monocular SLAM. IFAC Intelligent Autonomous Vehicles Symposium, 2013, Gold Coast, Australia. ⟨10.3182/20130626-3-AU-2035.00049⟩. ⟨hal-01351419⟩

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