Iterative equivariant filter improves mobile robot localization accuracy
The Iterative Equivariant Filter
Robotics
Summary
Estimating a robot's position accurately can be tricky, especially when measurements are noisy and the math involved is complex. This paper introduces a new method called the iterative equivariant filter that combines two earlier approaches to make better corrections when estimating position. The authors show that this method improves accuracy, especially when the robot is initially uncertain about its location. They demonstrate its benefits through computer simulations of a robot using range measurements to find its way.
What this means in practice
- •For robotics engineers: Enhance localization algorithms for mobile robots navigating with range sensors by implementing the iterative equivariant filter for improved convergence and accuracy.
- •For autonomous drone developers: Improve state estimation during navigation by adopting iterative equivariant filtering techniques that better handle nonlinearities in sensor data.
Authors
Pieter van Goor, James Richard Forbes
Abstract
This paper presents the iterative equivariant filter (IterEqF). The iterative extended Kalman filter (IterEKF) replaces the standard EKF correction step with an iterative correction step that is the Gauss-Newton solution to a nonlinear weighted least squares problem. The standard equivariant filter (EqF) exploits and respects the underlying symmetry of state estimation problems posed on homogeneous spaces and Lie groups. The motivation behind the IterEqF is to combine the features of both the IterEKF and the EqF, thus leading to a high-performance state estimation solution that is well suited to navigation problems. This paper derives the iteration procedure for the IterEqF update step and shows how the intrinsic nonlinearity of the approach naturally results in a `reset' of the filter's covariance into new coordinates. Monte-Carlo simulations of range-based localisation for a mobile robot demonstrate the improvement in performance relative to a standard EqF, especially during the transient convergence.