6 accepted papers
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 under
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 i…
Localization safety, or integrity risk, is the probability of undetected localization failures and a common aviation performance metric used to verify a minimum accuracy requirement. As autonomous robots become more common, applying integrity risk metrics will be necessary to verify localization per
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…
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…
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 algorit…