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 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.

[1]  Hadi Sadoghi Yazdi,et al.  Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise , 2016, 2016 Annual Conference on Information Science and Systems (CISS).

[2]  Maria V. Kulikova.  Chandrasekhar-Based Maximum Correntropy Kalman Filtering With the Adaptive Kernel Size Selection , 2020, IEEE Transactions on Automatic Control.

[3]  Zhenyu Huang,et al.  Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation , 2017, 2017 IEEE Power & Energy Society General Meeting.

[4]  Yonggang Zhang,et al.  A Novel Robust Student's t-Based Kalman Filter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Yulong Huang,et al.  A Slide Window Variational Adaptive Kalman Filter , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  Bo Xu,et al.  Improved Maximum Correntropy Cubature Kalman Filter for Cooperative Localization , 2020, IEEE Sensors Journal.

[8]  Thomas Lew,et al.  NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge , 2021, ArXiv.

[9]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[10]  Benjamin Morrell,et al.  Towards Resilient Autonomous Navigation of Drones , 2020, ISRR.

[11]  Giuseppe Loianno,et al.  Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors , 2017, Autonomous Robots.

[12]  M. V. Kulikova,et al.  Square-root algorithms for maximum correntropy estimation of linear discrete-time systems in presence of non-Gaussian noise , 2016, Syst. Control. Lett..

[13]  Anup Goyal,et al.  Extended Kalman Filter vs. Error State Kalman Filter for Aircraft Attitude Estimation , 2011 .

[14]  Michael Muma,et al.  Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts , 2012, IEEE Signal Processing Magazine.

[15]  Xi Liu,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[16]  A. H. Mohamed,et al.  Adaptive Kalman Filtering for INS/GPS , 1999 .

[17]  Lubin Chang,et al.  Unified Form for the Robust Gaussian Information Filtering Based on M-Estimate , 2017, IEEE Signal Processing Letters.

[18]  C. Guaragnella,et al.  LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time , 2020, IEEE Robotics and Automation Letters.

[19]  Yonggang Zhang,et al.  Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise , 2017, J. Frankl. Inst..

[20]  Yonggang Zhang,et al.  A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices , 2018, IEEE Transactions on Automatic Control.