Incremental Mapping with Measurement Synchronization & Compression

2025-12-02

International Conference on Advanced Robotics

AI Summary

This work addresses the inconsistency in state estimation and computational inefficiency arising from the rigid structure of factor graphs under asynchronous multi-sensor measurements. To overcome these limitations, the authors propose an incremental dynamic factor graph construction method that incorporates an external evaluation criterion to select the optimal graph topology in real time. This approach enables synchronous fusion of asynchronous multi-source sensor data and supports on-the-fly graph compression to reduce the number of optimization variables. Experimental results demonstrate that the proposed method maintains map accuracy comparable to conventional approaches while reducing the average number of graph nodes by approximately 30%, thereby significantly lowering computational complexity. The key innovation lies in the evaluation-driven dynamic topology construction and compression mechanism, which effectively balances estimation accuracy and computational efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Graphical Models
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Large-scale security measurements

Abstract

Modern autonomous vehicles and robots utilize versatile sensors for localization and mapping. The fidelity of these maps is paramount, as an accurate environmental representation is a prerequisite for stable and precise localization. Factor graphs provide a powerful approach for sensor fusion, enabling the estimation of the maximum a posteriori solution. However, the discrete nature of graph-based representations, combined with asynchronous sensor measurements, complicates consistent state estimation. The design of an optimal factor graph topology remains an open challenge, especially in multi-sensor systems with asynchronous data. Conventional approaches rely on a rigid graph structure, which becomes inefficient with sensors of disparate rates. Although preintegration techniques can mitigate this for high-rate sensors, their applicability is limited. To address this problem, this work introduces a novel approach that incrementally constructs connected factor graphs, ensuring the incorporation of all available sensor data by choosing the optimal graph topology based on the external evaluation criteria. The proposed methodology facilitates graph compression, reducing the number of nodes (optimized variables) by ∼30% on average while maintaining map quality at a level comparable to conventional approaches.

Problem

• incremental mapping
• factor graph
• asynchronous sensors
• graph topology
• sensor fusion

Innovation

• incremental mapping
• factor graph
• measurement synchronization
• graph compression
• multi-sensor fusion