A Smart-Scheduled Hybrid (SSH) EKF-FGO State Estimation
- URL: http://arxiv.org/abs/2606.16057v2
- Date: Thu, 18 Jun 2026 17:07:20 GMT
- Title: A Smart-Scheduled Hybrid (SSH) EKF-FGO State Estimation
- Abstract summary: This paper presents an experimental study that explicitly isolates optimisation scheduling using a Smart Scheduled Hybrid (SSH) EKF-FGO framework as a testbed.<n>By combining EKF-based state propagation with periodically invoked batch optimisation and holding solver structure and effort fixed, the main contribution is the experimental characterisation of optimisation scheduling as an independent design variable.<n>Results identify operating regimes in which most of the benefit of global optimisation can be retained at a fraction of the computational cost.
- Score: 1.3750624267664155
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reliable state estimation in robotics and control re quires balancing estimation accuracy against computational cost. While filtering-based methods such as the Extended Kalman Filter (EKF) provide efficient real-time updates, and optimisation based formulations using factor graphs improve global consistency, the role of optimisation scheduling is often treated implicitly rather than examined as an explicit design variable. This paper presents an experimental study that explicitly isolates optimisation scheduling using a Smart Scheduled Hybrid (SSH) EKF-FGO framework as a controlled testbed. By combining EKF-based state propagation with periodically invoked batch optimisation and holding solver structure and effort fixed, the main contribution of this work is the experimental characterisation of optimisation scheduling as an independent design variable governing the trade-off between intermediate estimation accuracy and computational cost. Simulation results in a planar SLAM environment show that scheduling strongly influences pre optimisation drift, transient error behaviour, and runtime. In particular, the results identify operating regimes in which most of the benefit of global optimisation can be retained at a fraction of the computational cost, highlighting optimisation scheduling as an under-explored yet critical consideration in hybrid state estimation systems.
Related papers
- The Right Call for Software Benchmarking: Consistent Decisions in Stateful Environments [1.049126606580198]
Modern computing systems rely on stateful mechanisms to accommodate the dynamics of workloads and physical environments.<n> adaptive mechanisms introduce temporal dependencies between measurements and render naive estimators of individual program performance biased.<n>We propose simple designs admitting consistent estimators of contrasts, whereby program-specific biases cancel under tenable assumptions.
arXiv Detail & Related papers (2026-06-15T20:11:47Z) - LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling [74.94985663101906]
We present LeapTS, a novel framework that reformulates time series forecasting as a dynamic scheduling process over the prediction horizon.<n>We show that LeapTS improves overall forecasting performance by at least 7.4% while achieving a 2.6$times$ to 5.3$times$ inference speedup over representative Transformer-based models.
arXiv Detail & Related papers (2026-05-11T09:54:02Z) - Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control [33.03635235189535]
We introduce Generative Control as Optimization (GeCO), a time-unconditional framework that transforms action synthesis into iterative optimization.<n>Test-time inference becomes an adaptive process that allocates computation based on convergence-exiting early for simple states while refining longer for difficult ones.<n>We validate GeCO on standard simulation benchmarks and demonstrate seamless scaling to pi0-series Vision-Language-Action (VLA) models.
arXiv Detail & Related papers (2026-03-18T15:27:17Z) - Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling [12.409081996599665]
The Agile Earth Observation Satellite Scheduling Problem (UAEOSSP) is a novel optimization problem.<n>It incorporates uncertainties in profit, resource consumption, and visibility, which may render pre-planned schedules suboptimal or even infeasible.<n>This paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) for effectively solving UAEOSSP.
arXiv Detail & Related papers (2026-03-09T14:43:36Z) - Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
arXiv Detail & Related papers (2026-03-04T14:48:53Z) - Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
arXiv Detail & Related papers (2026-02-01T12:56:10Z) - Low-Dimensional Adaptation of Rectified Flow: A New Perspective through the Lens of Diffusion and Stochastic Localization [59.04314685837778]
Rectified flow (RF) has gained considerable popularity due to its generation efficiency and state-of-the-art performance.<n>In this paper, we investigate the degree to which RF automatically adapts to the intrinsic low dimensionality of the support of the target distribution to accelerate sampling.<n>We show that, using a carefully designed choice of the time-discretization scheme and with sufficiently accurate drift estimates, the RF sampler enjoys an complexity of order $O(k/varepsilon)$.
arXiv Detail & Related papers (2026-01-21T22:09:27Z) - Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management [0.913755431537592]
We exploit tree-based boosting gradient (XGBoost) trained on eight years of gas meter maintenance data to produce point predictions and uncertainty estimates.<n>Our results report improvements around 20-25% in operator completion rates compared with plans computed using default durations.
arXiv Detail & Related papers (2026-01-12T13:12:46Z) - Dynamic Learning Rate Scheduling based on Loss Changes Leads to Faster Convergence [2.1665689529884697]
emphGreedyLR is a novel scheduler that adaptively adjusts the learning rate during training based on the current loss.<n>Our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence.
arXiv Detail & Related papers (2025-12-16T16:03:52Z) - Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models [0.479839492673697]
The Hierarchical Evaluation Function (HEF) is proposed as a multi-metric framework for hyperparameter optimization.<n>HEF integrates explanatory power (R2), sensitivity to extreme errors (RMSE), and average accuracy (MAE)<n>The performance of HEF was assessed using four widely recognized benchmark datasets in the forecasting domain.
arXiv Detail & Related papers (2025-08-18T16:25:49Z) - Stochastic Optimization with Optimal Importance Sampling [49.484190237840714]
We propose an iterative-based algorithm that jointly updates the decision and the IS distribution without requiring time-scale separation between the two.<n>Our method achieves the lowest possible variable variance and guarantees global convergence under convexity of the objective and mild assumptions on the IS distribution family.
arXiv Detail & Related papers (2025-04-04T16:10:18Z) - Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management [50.34345101758248]
We propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions.<n>Our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency.<n>Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
arXiv Detail & Related papers (2025-02-25T16:15:35Z)
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.