Safe navigation of autonomous vehicles relies on high integrity localization system based on a state estimation method, which strongly depends on the choice of parameters, such as the measurement noise covariance matrix. To avoid filter divergence, a fault detection and exclusion (FDE) procedure is also required. Selecting appropriate thresholds for the FDE step is challenging and affects the accuracy and the integrity of localization. This paper presents a multi-sensor data fusion based on the Information Filter (IF) with an auto-tuning method that relies on Bayesian Optimization (BO). The proposed method aims to optimize the measurement covariance matrix and the FDE thresholds. BO is well suited for non-convex and stochastic cost functions with local minima. A novel objective function is also designed to improve the accuracy and guarantee the integrity of the estimates, by ensuring consistent uncertainty regions. The objective function consists of two terms: one related to the error and the other to the quantile. The proposed approach is evaluated with experimental data from a vehicle equipped with wheel speed sensors, fused with Global Navigation Satellite System (GNSS) pseudorange measurements.

The navigation of intelligent vehicles relies on high integrity localization system capable to bound the estimation errors. This paper introduces a zonotopic and Gaussian Kalman filter in informational form for multi-sensor data fusion and confidence domain computation. By integrating stochastic and set membership uncertainties, the proposed filter ensures accurate localization with a non pessimistic confidence domain, thus addressing the challenges posed by traditional techniques. Taking advantage of the informational form, a fault detection and exclusion step is added to enhance filter robustness. Following a zonotope reduction step, a confidence domain computation, considering both Gaussian and zonotopic uncertainties, is proposed in the context of intelligent vehicles. The accuracy and integrity of the approach are assessed using experimental data, including the fusion of GPS and Galileo pseudoranges with camera measurements after a map matching step. Additionally, a comparative analysis is conducted with the classical Kalman filter.

This paper presents a strategy to combine a fault tolerant multi-sensor data fusion with a closed loop controller scheme robust against external disturbances applied to a multi-robot mobile system tracking different trajectories. Multi-sensor fusion is ensured using an information filter which is the canonical form of the Kalman filter. Fault detection and exclusion strategy is proposed to eliminate any erroneous measurements, for this purpose, risiduals are generated using the Kullback-Leibler diveregence that compares the priori and posteriori distributions of the predicted and the corrected estimations respectively. Prediction model is based on odometry, with encoders data as input. Observation model is based on extra sensors observations. To optimise detections, an adaptive thresholding method based on the Kullback-Leibler Criteron is proposed. Trajectory tracking is achieved using a sliding mode controller (SMC), developped by inverting the dynamical model of mobile robots. SMC is robust against external matched disturbances, parameters variation and actuators detoriation, however it can not handle total loss of effectiveness, which makes detection and isolation of faulty actuators compulsory. For this purpose, controllers inputs are converted into expected elementary velocities and compared to real data obtained from fault tolerant multi-sensor data fusion. The main contribution of this paper is to combine a fault detection and exclusion (FDE) scheme with an enhanced sliding mode controller in order to detect and isolate both sensors and actuators faults. The method is applied to a multi-robot system. The obtained experimental results demonstrate the egibility and the effectiveness of the proposed approach.