Abstract
This paper presents a real-time Consistent Monocular 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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