Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems
This paper introduces a novel invariant Kalman filtering approach for extended pose estimation in multi-IMU articulated rigid-body systems. By proposing a relative L-extended pose Lie group representation and incorporating joint kinematic constraints as noise-free pseudo-measurements within an iterated IEKF, the method achieves faster convergence and over 50% reduction in RMSE compared to existing filters on both a UR5e robot and a human leg.
[2606.25083] Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems
[Submitted on 23 Jun 2026]
Title:Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems
View a PDF of the paper titled Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems, by Sven Goffin and 4 other authors
View PDF HTML (experimental)
Abstract:Accurate extended pose estimation (orientation, velocity, and position) for IMU-instrumented articulated rigid-body systems is a key challenge in robotics and human motion analysis. The invariant extended Kalman filter (IEKF) addresses this problem for a single rigid body with convergence guarantees and consistency under unobservability, but extending these properties to articulated systems is nontrivial: inter-body pose coupling prevents a direct application, and incorporating joint kinematic constraints within the invariant framework remains an open problem. To address this gap, we introduce the relative L-extended pose, a Lie group representation for kinematic-tree systems. With one IMU per body, it yields group-affine dynamics and allows joint constraints to be expressed in invariant form. We incorporate these constraints as noise-free pseudo-measurements within an iterated IEKF (IterIEKF), thereby preserving the convergence and consistency guarantees of invariant filtering. Validated on both a UR5e robot and a human leg, the proposed IterIEKF outperforms all EKF, IterEKF, and absolute-pose IterIEKF baselines. It converges faster, exhibits lower run-to-run variability, and consistently achieves the lowest RMSE, with reductions of at least 50% compared to the second-best filter across all scenarios considered in this work.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.25083 [cs.RO]
(or arXiv:2606.25083v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.25083
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sven Goffin [view email] [v1] Tue, 23 Jun 2026 18:41:31 UTC (1,439 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems, by Sven Goffin and 4 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)