Visualization of Possibilistic Filtering for Reliable Robot Localization

The video presents experimental results of a possibilistic filter applied to a real-world robot localization task using a particle-based implementation. The objective is to identify possible robot states based on the underlying dynamics and angular measurements obtained from a fisheye camera, which determines the robot’s orientation relative to four landmarks distributed throughout the environment. This setup involves an intricate combination of aleatory and epistemic uncertainty in the measurement process: the combination of angles can lead to highly ambiguous pose estimates, measurements are affected by nominal sensor noise, and the robot may occasionally fail to detect landmarks altogether. The results demonstrate that the filter is capable of providing reliable state estimates even in highly ambiguous and uncertain localization scenarios. The dataset, consisting of the true states and corresponding measurements, is attached as well.