期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：7
页码：14100
DOI：10.15680/IJIRSET.2017.0607194
出版社：S&S Publications
摘要：This paper introduces a state estimation framework for legged robots that allows estimating thefull pose of the robot without making any assumptions about the geometrical structure of its environment.This is achieved by means of an Observability Constrained Extended Kalman Filter that fuses kinematicencoder data with on-board IMU measurements. By including t h e absolute p o s i t i o n of all footholds i n t othe filter state, simple model equations can be formulated which accurately capture the uncertainties associatedwith the intermittent ground contacts. The resulting filter simultaneously estimates the position of all footholdsand the pose of the main body. In the algorithmic formulation, special attention is paid to the consistency ofthe linearized filter: it maintains the same observability properties as the nonlinear system, which is aprerequisite for accurate state estimation. The presented approach is implemented in simulation and validatedexper imentall y on an actual quadruped AL robot.
