SLOT-MPC: A Hierarchical Whole-Body Model Predictive Controller to Enhance Simultaneous Localization and Object Tracking for UAVs

This paper proposes SLOT-MPC, a hierarchical model predictive control framework for a system of multirotor Unmmaned Aerial Vehicle (UAV), which aims to minimize uncertainty in estimating both ego-motion and a moving object, thus enhancing the performance of Simultaneous Localization and Object Tracking (SLOT). The framework consists of two layers: OT-MPC (Object Tracking-Model Predictive Controller) for high-level path planning, and a full model whole-body SL-MPC (Self Localization-Model Predictive Controller) for path tracking and view control. The OT-MPC uses a point-mass model with a proposed time-continuous information filter to minimize object estimation uncertainty and computes optimal chasing paths online in a receding horizon manner. Subsequently, to improve visual-based self-localization, the SL-MPC is developed to track the path generated by the OT-MPC, while simultaneously optimizing perception objectives considering observed features to improve visual-based localization. Thus, optimal control sequences for the aerial vehicle are obtained in real time. Experiments are performed to validate the practicability of our approach. We will release our implementation as an open source package for the community.