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International audience; Tractable algorithms used for 6DOF visual-inertial odometry have decades-long history of estimation consistency issues. Those arise in particular in two well-studied filters: namely the EKF-SLAM and MSCKF. Recently, strong theoretical works linked the error-state of these filters with their consistency properties; these results led to the synthesis of far more consistent filters. In previous works, we have shown that using similar filter for the fusion of magneto-inertial sensors with optical ones improved classical visual-inertial navigation systems. The consistency of those novel magneto-visual-inertial filters were, however, not addressed until now. This work does. We apply invariance theory findings to the specific case of magneto-inertial odometry and magneto-visual-inertial odometry for the synthesis of a filter with interesting consistency properties. We describe thoroughly such an invariant filter, implement it and conduct experiments on carefully captured data from real sensor. By comparing the results of non-invariant, observability-constrained and invariant versions of the filter, we find that the invariant version (i) shows an error estimate that is consistent with observability of the system, (ii) is applicable in case of unknown heading at initialization, (iii) improves long-term behavior of the filter and (iv) exhibits a lower normalized estimation error. We experiment on challenging scenarios for regular visual-inertial pedestrian navigation systems.

International audience; Visual-inertial Navigation Systems (VINS) are nowadays used for robotic or augmented reality applications. They aim to compute the motion of the robot or the pedestrian in an environment that is unknown and does not have specific localization infrastructure. Because of the low quality of inertial sensors that can be used reasonably for these two applications, state of the art VINS rely heavily on the visual information to correct at high frequency the drift of inertial sensors integration. These methods struggle when environment does not provide usable visual features, such than in low-light of texture-less areas. In the last few years, some work have been focused on using an array of magnetometers to exploit opportunistic stationary magnetic disturbances available indoor in order to deduce a velocity. This led to Magneto-inertial Dead-reckoning (MI-DR) systems that show interesting performance in their nominal conditions, even if they can be defeated when the local magnetic gradient is too low, for example outdoor. We propose in this work to fuse the information from a monocular camera with the MI-DR technique to increase the robustness of both traditional VINS and MI-DR itself. We use an inverse square root filter inspired by the MSCKF algorithm and describe its structure thoroughly in this paper. We show navigation results on a real dataset captured by a sensor fusing a commercial-grade camera with our custom MIMU (Magneto-inertial Measurment Unit) sensor. The fused estimate demonstrates higher robustness compared to pure VINS estimate, specially in areas where vision is non informative. These results could ultimately increase the working domain of mobile augmented reality systems.

International audience; This paper presents a complete mobile robot architecture for autonomous exploration in a GPS-denied unknown environment. The platform considered is equipped with wheel encoders, stereo-vision and depth sensors, the measurements of which are fused within an extended Kalman filter for robust localization. An occupancy grid of the environment is built on-line for environment reconstruction and obstacle detection. Based on this map, a model predictive control scheme autonomously defines safe exploration trajectories, while taking into account interaction with the imaging sensors. Experimental results demonstrate the embedded computational capability of this vision-based control loop.