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.

In urban environments where GNSS performance is degraded, localization can be performed using stable and geo-referenced map features detected by on-board sensors. Prior maps are prone to errors which have a direct impact on localization accuracy. By exchanging observed features and sharing their maps, vehicles can simultaneously improve their localization and update the map. This paper deals with indirect collaboration, where vehicles do not observe each other directly. The features are obtained from building facades using 3D lidar sensors. The paper emphasizes real-time decentralized collaboration with direct communication between vehicles, without the need for a central server. The collaboration takes place when vehicles perceive the same geo-referenced facades. Vehicle poses and maps are collaboratively updated using a Schmidt Kalman filter that carefully manages the cross-covariance terms. To maintain consistent estimates, the Kullback-Leibler Average is used. We also present a lidar data processing pipeline to obtain reliable observations from building facades. Real tests carried out with experimental vehicles on the university campus are reported. The results show that indirect collaboration makes a significant contribution to localization and map update when compared to a standalone method.

Localization integrity consists in providing a real-time measure of the level of trust to be placed in the localization estimates as vehicles operate. It provides a means of knowing whether position estimates are usable for navigation purposes. This paper formalizes the integrity concept and its underlying principles. Vehicles operate in different navigation environments, and so multiple sensors are used to ensure the required performance. Different sources of error exist. They must be bounded according to the acceptable level of risk for the application. This paper presents a generic approach for addressing integrity. It combines measurement rejection (for measurements considered to be faults) and position error characterization. For this purpose, a multi-sensor data fusion with a Fault Detection and Exclusion algorithm is constituted using a bank of information filters. These filters allow detected faults to be isolated without any prior assumption regarding the number of simultaneous errors. In addition, external integrity is expressed as a Protection Level of the localization solution. It uses a Student’s$t$-distribution in order to bound the distribution of the position error applicable to small integrity risks after a learning step. The approach is tested on data acquired on public roads using an experimental vehicle equipped with off-the-shelf proprioceptive and exteroceptive sensors together with an HD map. The results obtained validate the proposed approach.

Localization with high integrity is crucial for highly autonomous vehicles. This requires that the localization system send a warning to a client application when it should not be used. The concept of integrity was firstly developed for aviation applications and recently became an active research area for autonomous vehicles. GNSS information merged with dead reckoning sensors is not sufficient for lane level localization in all navigation environments. Map-aided localization with vision sensors is essential to provide redundant and complementary information. In this work, a multi-sensor data fusion method that takes advantage of a high definition (HD) map is presented and the integrity of the obtained solution is quantified. A Fault Detection and Exclusion (FDE) step is added to exclude the faulty measurements from the fusion procedure. A second step is to bound the estimation errors in the Along Track (AT) and Cross Track (CT) directions through Protection Levels (PL). For this step, the usual Gaussian distribution is replaced by a Student's distribution with an adapted degree of freedom chosen according to the navigation environment. The performance of the approach is evaluated with an experimental vehicle equipped with a camera able to detect up to four lane markings simultaneously.

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.