Open AccessPosted Content
An EKF-SLAM algorithm with consistency properties.
Axel Barrau,Silvere Bonnabel +1 more
TLDR
This paper addresses the inconsistency of the EKF-based SLAM algorithm that stems from non-observability of the origin and orientation of the global reference frame and proves on the non-linear two-dimensional problem with point landmarks that this type of inconsistency is remedied using the Invariant EKf.Abstract:
In this paper we address the inconsistency of the EKF-based SLAM algorithm that stems from non-observability of the origin and orientation of the global reference frame. We prove on the non-linear two-dimensional problem with point landmarks observed that this type of inconsistency is remedied using the Invariant EKF, a recently introduced variant ot the EKF meant to account for the symmetries of the state space. Extensive Monte-Carlo runs illustrate the theoretical results.read more
Citations
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Journal ArticleDOI
Contact-aided invariant extended Kalman filtering for robot state estimation:
TL;DR: This work develops a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design and demonstrates how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions.
Journal ArticleDOI
Convergence and Consistency Analysis for a 3-D Invariant-EKF SLAM
TL;DR: Investigation of the convergence and consistency properties of an invariant-extended Kalman filter based simultaneous localization and mapping (SLAM) algorithm proves that the output of RI-EKF is invariant under any stochastic rigid body transformation, and implications of these invariance properties on the consistency of the estimator are discussed.
Journal ArticleDOI
Consistent EKF-Based Visual-Inertial Odometry on Matrix Lie Group
Sejong Heo,Chan Gook Park +1 more
TL;DR: A novel visual-inertial navigation algorithm for low-cost and computationally constrained vehicle in global positioning system denied environments is presented by modeling the state space as the matrix Lie group (LG), based on the recent theory of the invariant Kalman filter.
Proceedings ArticleDOI
Unscented Kalman filtering on Lie groups
TL;DR: The general method is applied to a robot localization problem, and results based on experimental data combined with extensive Monte-Carlo simulations at various noise levels illustrate the superiority of the approach over the standard UKF.
Posted Content
Convergence and Consistency Analysis for A 3D Invariant-EKF SLAM
TL;DR: In this paper, the convergence and consistency properties of an invariant-extended Kalman filter (RI-EKF) based SLAM algorithm are investigated and shown to be invariant under any stochastic rigid body transformation.
References
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Book
Probabilistic Robotics
TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
Book
Estimation with Applications to Tracking and Navigation
TL;DR: Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
Journal ArticleDOI
Simultaneous localization and mapping: part I
TL;DR: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method.
Journal ArticleDOI
A solution to the simultaneous localization and map building (SLAM) problem
TL;DR: The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
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