The invariant extended Kalman filter as a stable observer
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Abstract (arXiv)
We analyze the convergence aspects of the invariant extended Kalman filter (IEKF), when the latter is used as a deterministic non-linear observer on Lie groups, for continuous-time systems with discrete observations. One of the main features of invariant observers for left-invariant systems on Lie groups is that the estimation error is autonomous. In this paper we first generalize this result by characterizing the (much broader) class of systems for which this property holds. Then, we leverage the result to prove for those systems the local stability of the IEKF around any trajectory, under the standard conditions of the linear case. One mobile robotics example and one inertial navigation example illustrate the interest of the approach. Simulations evidence the fact that the EKF is capable of diverging in some challenging situations, where the IEKF with identical tuning keeps converging.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Oct 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 3; 2026: 7.
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- 2023Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebrascited by 6
- 2024Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Networkcited by 4
- 2025Equivariant Neural Networks for General Linear Symmetries on Lie Algebrascited by 4
- 2022Towards Accurate Ground Plane Normal Estimation from Ego-Motioncited by 2
- 2025DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoringcited by 2
- 2025GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residualscited by 1
- 2021Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processescited by 0
- 2026The Geometric Observability Index: Influence, Fisher Information, and Weak Observability in SE(3) Pose Estimationcited by 0
- 2026POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometrycited by 0
- 2026K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groupscited by 0
- 2026The Transformer as a Polar State Estimatorcited by 0
- 2026Hierarchical Variational Kalman Filteringcited by 0
2 more
- 2026GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry21 Sep 2026
- 2026Learning Forced Multibody Dynamics on Lie Groups14 Jul 2026
- 2026Hierarchical Variational Kalman Filtering1 Jul 2026
- 2026The Transformer as a Polar State Estimator10 May 2026
- 2026K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groups20 Mar 2026
- 2026POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry6 Feb 2026
- 2026The Geometric Observability Index: Influence, Fisher Information, and Weak Observability in SE(3) Pose Estimation5 Feb 2026
- 2025Equivariant Neural Networks for General Linear Symmetries on Lie Algebras27 Oct 2025
- 2025DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring17 Sep 2025
- 2025GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals13 Jun 2025
- 2024Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network1 Feb 2024
- 2023Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras6 Oct 2023
- 2023Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebrascited by 6
- 2024Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Networkcited by 4
- 2025Equivariant Neural Networks for General Linear Symmetries on Lie Algebrascited by 4
- 2022Towards Accurate Ground Plane Normal Estimation from Ego-Motioncited by 2
- 2025GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residualscited by 1
- 2021Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processescited by 0
- 2026The Geometric Observability Index: Influence, Fisher Information, and Weak Observability in SE(3) Pose Estimationcited by 0
- 2026POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometrycited by 0
- 2026K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groupscited by 0
- 2026The Transformer as a Polar State Estimatorcited by 0
- 2026Hierarchical Variational Kalman Filteringcited by 0
- 2026Learning Forced Multibody Dynamics on Lie Groupscited by 0
12 most cited of 13 All 13 (JSON)
- 2026GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry21 Sep 2026
- 2026Learning Forced Multibody Dynamics on Lie Groups14 Jul 2026
- 2026Hierarchical Variational Kalman Filtering1 Jul 2026
- 2026The Transformer as a Polar State Estimator10 May 2026
- 2026K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groups20 Mar 2026
- 2026POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry6 Feb 2026
- 2026The Geometric Observability Index: Influence, Fisher Information, and Weak Observability in SE(3) Pose Estimation5 Feb 2026
- 2025Equivariant Neural Networks for General Linear Symmetries on Lie Algebras27 Oct 2025
- 2025GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals13 Jun 2025
- 2024Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network1 Feb 2024
- 2023Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras6 Oct 2023
- 2022Towards Accurate Ground Plane Normal Estimation from Ego-Motion8 Dec 2022
12 newest of 13 All 13 (JSON)