Connected autonomous vehicles (CAVs) can provide benefits over individual vehicles for precise navigation, especially in GNSS-denied environments. CAV collaboration can enhance estimation accuracy, but the safety of collaborative localization in the presence of undetected sensor faults remains underexplored. This paper introduces an integrity monitoring method for CAV collaborative localization in both centralized and decentralized implementations. Fault models for landmark and relative measurements are described, and the probability of hazardous misleading information, or integrity risk, is derived. Simulation and experimental results for notional two-CAV scenarios indicate that collaborative localization reduces integrity risk and enhances navigation safety.

Deriving safe bounds on particle filter estimate is a research problem that, if solved, could greatly benefit robots in life-critical applications, a field that is facing increasing interest as more robots are being deployed near humans. In response, this article introduces a new fault detector and derives a performance measure for particle filter: integrity risk. Integrity risk is defined as the probability of having large estimate errors without triggering an alarm, all while considering measurement faults, unknown deterministic errors that cannot be modeled via normal white noise. In this work, the faults come in the form of incorrectly associated features when using the local nearest neighbors. Simulations and experiments assess the efficiency of the introduced safety metric. The results show that safety improves as map density increases as long as the number of particles is sufficient to shape the error distribution and the landmarks are well separated. Also, the results indicate that, when landmarks are poorly separated, particle filter is safer than Kalman filter, whereas, when landmarks are well separated, particle filter is often, but not always, safer than Kalman filter.

In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light Detection and Ranging (LiDAR) localization algorithm that includes uncertainty quantification for ground vehicle navigation. This paper builds upon prior research where we used a CNN to estimate a rover’s position and orientation (pose) using LiDAR point clouds (PCs). This paper presents a simplification of the LiDAR PC processing and describes a new approach for outputting a covariance matrix in addition to the rover pose estimates. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory.

This paper presents a method to validate localization safety for a preplanned trajectory in a given environment. Localization safety is defined as integrity risk and quantified as the probability of an undetected localization failure. Integrity risk differs from previously used metrics in robotics in that it accounts for unmodeled faults and evaluates safety under the worst possible combination of faults. The methodology can be applied prior to mission execution and thus can be employed to evaluate the safety of potential trajectories. The work has been formulated for localization via smoothing, which differs from previously reported integrity monitoring methods that rely on Kalman filtering. Simulation and experimental results are analyzed to show that localization safety is effectively quantified.

This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults.

This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety.

This paper describes the design of a new integrity risk prediction/monitoring methodology for robot localization that uses feature extraction and data association algorithms. The work specifically addresses incorrect association faults when employing a local nearest neighbor data association algorithm. This approach is more efficient and easier to implement than previous work. The methodology is tested in simulation, showing that the computed upper bound on integrity risk is a performance metric capable of providing warnings when the safety of the system cannot be guaranteed.