Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay

• URL: http://arxiv.org/abs/2605.13266v1
• Date: Wed, 13 May 2026 09:46:18 GMT
• Title: Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay
• Abstract summary: Commercial-grade receivers introduce unknown measurement delays ranging from 50 ms to 300 ms depending on sensor quality and operating mode.<n>Existing algorithms commonly estimate this delay offline, run the filter concurrently with measurements using buffered Inertial Measurement Unit (IMU) data.<n>This paper introduces a novel geometric framework for modeling time-delayed INS, in which Galilean symmetry is leveraged to provide a joint representation of space and time.
• Score: 27.48917642274488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Inertial Navigation Systems (INS) use Global Navigation Satellite System (GNSS) position as the primary measurement to drive filter performance and bound error growth. However, commercial-grade GNSS receivers introduce unknown measurement delays ranging from 50 ms to 300 ms depending on sensor quality and operating mode. Such time delays can significantly degrade INS performance unless they are explicitly compensated for. Existing algorithms commonly estimate this delay offline, run the filter concurrently with GNSS measurements using buffered Inertial Measurement Unit (IMU) data, and predict the current state by forward-integrating buffered inertial measurements via IMU preintegration. The state-of-the-art online method is an Extended Kalman Filter (EKF) that explicitly models the time delay as a state parameter, which defines the preintegration duration. This paper introduces a novel geometric framework for modeling time-delayed INS, in which Galilean symmetry is leveraged to provide a joint representation of space and time for consistent state estimation. An Equivariant Filter (EqF) is derived for the coupled estimation of navigation states and time delay. Validation is performed on two fixed-wing Uncrewed Aerial Vehicles (UAV) with GNSS time lags of 90 ms and 120 ms. The test flights last two to three minutes. Simulations further investigate delays up to 500 ms and provide a statistical comparison against the state-of-the-art EKF. Results show that the EqF preserves accuracy and consistency, while the EKF lacks consistency and its performance degrades significantly with increasing measurement delays.

Related papers

• A Sliding Window Filter on the Galilean Group for Consistent Aided Inertial Navigation with Unknown Measurement Delays [9.474491558495359]
  We study aided navigation when the aiding sensor measurements are subject to an unknown constant delay.<n>The goal is to estimate the delay and navigation state jointly so that delayed measurements correct the trajectory at the appropriate times.
  arXiv  Detail & Related papers  (2026-08-30T02:34:28Z)
• Flight-Ready LiDAR-Inertial Odometry for Embedded Drone Platforms [0.0]
  Open-source LiDAR-inertial odometry systems have achieved remarkable benchmark accuracy.<n>Current state-of-the-art implementations are primarily optimized for evaluation performance.
  arXiv  Detail & Related papers  (2026-07-24T09:44:29Z)
• FAST-LIVGO: A Degeneracy-Robust LiDAR-Inertial-Visual-GNSS Fusion Odometry [14.641801914375163]
  State estimation and mapping in long-term, large-scale, and highly dynamic environments remains a key challenge in robotics.<n>Existing LiDAR-In-ertial-Visual Odometry (LIVO) systems achieve strong local accuracy but suffer from accumulated drift over long distances.<n>We propose a LiDAR-aided fusion framework based on an Error-State Iterated Kalman Filter to address these limitations.<n>Our system reduces accumulated drift and map ghosting, outperforming state-of-the-art methods in accuracy and robustness.
  arXiv  Detail & Related papers  (2026-06-17T15:33:11Z)
• Equivariant Filter for Radar-Inertial Odometry [45.85581416235741]
  Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU)<n>This letter proposes an Equivariant Filter (EqF) for RIO based on a Lie group symmetry that geometrically couples navigation states and IMU biases.
  arXiv  Detail & Related papers  (2026-04-24T21:47:06Z)
• An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays [22.41476754687808]
  In-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from dead-reckoning drift.<n>Traditional filters, such as Extended Kalman Filters (EKF) or Unscented Kalman Filters (UKF), usually block the main control loop while waiting for delayed data.<n>We propose an Asynchronous Two-Speed Kalman Filter (T) enhanced by a novel projection mechanism, which we term Variational History Distillation (VHD)
  arXiv  Detail & Related papers  (2026-04-03T08:43:25Z)
• Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs [46.21996481591401]
  This paper proposes a consistent and efficient LiDAR-Inertial Odometry framework tailored for UAVs.<n>By applying null-space projection to sliding-window coplanar constraints, we eliminate the direct dependency on feature parameters in the state vector.<n>It exhibits improved robustness in degenerate scenarios, achieves the lowest memory usage via its map-free nature, and runs in real-time on resource-constrained embedded platforms.
  arXiv  Detail & Related papers  (2026-03-13T11:09:50Z)
• GNSS-based Lunar Orbit and Clock Estimation With Stochastic Cloning UD Filter [0.0]
  This paper presents a terrestrial-based orbit and clock estimation framework for lunar satellites.<n>We develop a Lunar-cloning UD-factorized filter and delayed-state smoother that provide enhanced numerical stability when processing precise time-differenced carrier phase (TDCP) measurements.
  arXiv  Detail & Related papers  (2026-01-23T01:54:35Z)
• Gaussian Process Regression for Improved Underwater Navigation [13.221163846643607]
  Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements.<n>This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation.<n>We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet.
  arXiv  Detail & Related papers  (2025-02-23T09:13:41Z)
• MTD: Multi-Timestep Detector for Delayed Streaming Perception [0.5439020425819]
  Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. This paper propose the Multi- Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings.
  arXiv  Detail & Related papers  (2023-09-13T06:23:58Z)
• Neural Laplace Control for Continuous-time Delayed Systems [76.81202657759222]
  We propose a continuous-time model-based offline RL method that combines a Neural Laplace dynamics model with a model predictive control (MPC) planner. We show experimentally on continuous-time delayed environments it is able to achieve near expert policy performance.
  arXiv  Detail & Related papers  (2023-02-24T12:40:28Z)
• FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization [78.46088089185156]
  Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. Existing approaches penalize emission delay by manipulating per-token or per-frame probability prediction in sequence transducer models. We propose a sequence-level emission regularization method, named FastEmit, that applies latency regularization directly on per-sequence probability in training transducer models.
  arXiv  Detail & Related papers  (2020-10-21T17:05:01Z)
• TLIO: Tight Learned Inertial Odometry [43.17991168599939]
  We propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty.
  arXiv  Detail & Related papers  (2020-07-06T03:13:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.