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.

翻译：现代自动驾驶车辆与机器人利用多功能传感器进行定位与建图。这些地图的保真度至关重要，因为准确的环境表征是实现稳定精确定位的前提。因子图为传感器融合提供了一种强有力的方法，能够估计最大后验解。然而，基于图表示的离散特性，结合异步传感器测量，使得一致的状态估计变得复杂。最优因子图拓扑的设计仍是一个开放挑战，尤其是在具有异步数据的多传感器系统中。传统方法依赖于固定的图结构，这在传感器频率差异较大时效率低下。尽管预积分技术可为高频传感器缓解此问题，但其适用性有限。为解决这一问题，本研究提出一种新颖方法，通过基于外部评估准则选择最优图拓扑，增量式构建连通因子图，确保所有可用传感器数据均被纳入。所提方法实现了图压缩，在保持地图质量与传统方法相当水平的同时，平均减少约30%的节点（优化变量）数量。