🤖 AI Summary
This work addresses the challenge of balancing estimation accuracy and computational cost in robotic state estimation by proposing a Smart Scheduling Hybrid (SSH) framework. The approach integrates an Extended Kalman Filter (EKF) for state propagation with periodic invocations of a fixed-structure batch optimization module. Crucially, the scheduling of optimization updates is explicitly modeled as an independent design variable, revealing its pivotal role in governing the trade-off between accuracy and computational expense. Validation on planar SLAM simulations demonstrates that well-designed scheduling strategies can substantially reduce runtime while effectively mitigating pre-optimization drift and transient errors. This enables the system to retain most of the benefits of global optimization at a significantly lower computational cost.
📝 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.