  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 general
non-Gaussian noise, and is able to modify filter parameters online to improve
performance. Two separate methods are developed -- the Variational Bayesian
AMCCKF (VB-AMCCKF) and Residual AMCCKF (R-AMCCKF) -- that modify the process
and measurement noise models in addition to the bandwidth of the kernel
function used in MCCKF based on the quality of measurements received. The two
approaches differ in computational complexity and overall performance which is
experimentally analyzed. The method is demonstrated in real experiments on both
aerial and ground robots and is part of the solution used by the COSTAR team
participating at the DARPA Subterranean Challenge.
