Dan Simon

Towards Robust State Estimation by Boosting the Maximum Correntropy Criterion Kalman Filter With Adaptive Behaviors

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or