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.

翻译：在机器人与控制领域，可靠的状态估计需要在估计精度与计算开销之间取得平衡。基于滤波的方法（如扩展卡尔曼滤波器，EKF）能提供高效的实时更新，而基于因子图的优化方法则可提升全局一致性，但优化调度的作用常被视为隐式影响因素，而非作为明确的设计变量加以研究。本文通过构建基于智能调度混合（SSH）的EKF-FGO框架作为受控测试平台，开展了一项实验研究，明确分离出优化调度的影响因素。该方法结合EKF状态传播与周期性触发的批处理优化，并保持求解器结构与计算量固定。本文的核心贡献在于通过实验表征了优化调度作为独立设计变量，在中间状态估计精度与计算开销之间权衡的作用。在平面SLAM环境中的仿真结果表明，调度策略对优化前的漂移、瞬态误差行为及运行时间均有显著影响。尤为重要的是，研究结果识别出了特定运行区间，在该区间内全局优化的绝大部分优势能以极低的计算开销保留，从而揭示了优化调度作为混合状态估计系统中一个尚未充分探索却至关重要的考量因素。