Normalizing Flow-Enhanced Message Passing for Multirobot Collaborative Localization

• URL: http://arxiv.org/abs/2606.29868v1
• Date: Mon, 29 Jun 2026 07:04:56 GMT
• Title: Normalizing Flow-Enhanced Message Passing for Multirobot Collaborative Localization
• Abstract summary: This paper proposes a new message passing (MP) algorithm for realizing collaborative localization in a distributed manner.<n>The algorithm unifies Gaussian belief propagation (GBP) and mean-field (MF) approximation, where GBP preserves dependencies among robot states, and MF enables estimation of noise statistics.<n> Simulations and experiments on autonomous surface vehicles (ASVs) demonstrate its improved accuracy, robustness, and adaptability.
• Score: 22.194428100020485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate, robust, and adaptive localization is essential for various robotic operations. This paper proposes a new message passing (MP) algorithm for realizing collaborative localization in a distributed manner. The algorithm unifies Gaussian belief propagation (GBP) and mean-field (MF) approximation, where GBP preserves dependencies among robot states, and MF enables estimation of noise statistics. To effectively handle non-conjugate terms from nonlinear measurement models, the algorithm adopts a parametric formulation in which these terms are treated by gradient estimators. Beyond linearization and sampling, we further design a normalizing flow (NF)-based gradient estimator, enabling learnable sampling. End-to-end training tunes NF parameters according to the behavior of MP, improving the overall estimation performance. To support estimation of practical robotic states that involve rotations, the method is then extended to Lie group state spaces. Finally, the method is applied to multirobot localization task fusing odometry, global navigation satellite system (GNSS) measurements, and inter-robot ultra wideband (UWB) ranging. Simulations and experiments on autonomous surface vehicles (ASVs) demonstrate its improved accuracy, robustness, and adaptability.

Related papers

• Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation [48.77928097573051]
  This paper presents an invertible neural network adapter for general robotic manipulation.<n>Built upon a flow-matching formulation, the proposed adapter constrains the action generation trajectory within an invertible latent space.<n>Experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2026-06-17T15:35:27Z)
• Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation [0.0]
  The proposed framework is robot-agnostic and is evaluated on the Unitree Go2 quadruped in simulation using Gazebo.<n>The full system achieves up to a 90.0% navigation success rate when deployed, along with a 38.9% average improvement in the evaluation metrics across simulated environments.
  arXiv  Detail & Related papers  (2026-06-08T16:08:09Z)
• Sampling-Based Control via Entropy-Regularized Optimal Transport [18.278741443124655]
  OT-MPC is a sampling-based algorithm that overcomes limitations through an entropy-regularized optimal transport formulation.<n>We derive closed-form, gradient-free updates via the Sinkhorn algorithm, enabling real-time performance.
  arXiv  Detail & Related papers  (2026-05-04T02:07:15Z)
• CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping [15.34037575218496]
  We propose a new continuous-time odometry method based on the cumulative form of B-splines.<n>We use forward propagation information from IMU measurements to estimate fitting errors online and introduce a hybrid feature-based voxel map management strategy.<n>The proposed method is evaluated on multiple challenging public datasets, demonstrating superior performance on most sequences.
  arXiv  Detail & Related papers  (2026-04-04T14:27:54Z)
• Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training [47.26632817047513]
  Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates.<n>We propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs.<n>Unlike conventional two-stage allocation methods, Reinforce-Ada interleaves estimation and sampling in an online successive elimination process.
  arXiv  Detail & Related papers  (2025-10-06T16:34:09Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure [3.6194127685460553]
  We develop a mobility-aware transformer-driven structure (MASSFormer) based cooperative sensing method. Our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) The proposed method is tested under imperfect reporting channel scenarios to show robustness.
  arXiv  Detail & Related papers  (2024-09-26T05:25:25Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients [54.90670513852325]
  We propose a non-uniform subsampling scheme to improve the sampling accuracy. EWSG is designed so that a non-uniform gradient-MCMC method mimics the statistical behavior of a batch-gradient-MCMC method. In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index.
  arXiv  Detail & Related papers  (2020-02-20T18:56:18Z)

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.