Adaptive approaches to nonlinear state estimation for mobile robot localization: an experimental comparison

2012 ◽  
Vol 35 (8) ◽  
pp. 971-985 ◽  
Author(s):  
James Richard Forbes
Keyword(s):  
Mobile Robot ◽  
State Estimation ◽  
Experimental Comparison ◽  
Robot Localization ◽  
Nonlinear State Estimation ◽  
Mobile Robot Localization ◽  
Nonlinear State ◽  
Adaptive Approaches

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

LMI Methods for Extended ℋ∞ Filters for Landmark-based Mobile Robot Localization

2021 ◽  
Author(s):  
Julio Fajardo ◽  
Victor Ferman ◽  
Jabes Guerra ◽  
Antonio Ribas Neto ◽  
Eric Rohmer
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Mobile Robot Localization
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