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Unmanned aerial vehicles (UAV) showed great potential in improving theefficiency of parcel delivery applications in the coming smart cities era.Unfortunately, the trustworthy positioning and control algorithms of the UAVare significantly challenged in complex urban areas. For example, theubiquitous global navigation satellite system (GNSS) positioning can bedegraded by the signal reflections from surrounding high-rising buildings,leading to significantly increased positioning uncertainty. An additionalchallenge is introduced to the control algorithm due to the complex winddisturbances in urban canyons. Given the fact that the system positioning andcontrol are highly correlated with each other, for example, the system dynamicsof the control can largely help with the positioning, this paper proposed ajoint positioning and control method (JPCM) based on factor graph optimization(FGO), which combines sensors' measurements and control intention. Inparticular, the positioning measurements are formulated as the factors in thefactor graph model, such as the positioning from the GNSS. The model predictivecontrol (MPC) is also formulated as the additional factors in the factor graphmodel. By solving the factor graph contributed by both the positioning factorand the MPC-based factors, the complementariness of positioning and control canbe fully explored. To guarantee reliable system dynamic parameters, we validatethe effectiveness of the proposed method using a simulated quadrotor systemwhich showed significantly improved trajectory following performance. Tobenefit the research community, we open-source our code and make it availableat https://github.com/RoboticsPolyu/IPN_MPC.

Tightly Joining Positioning and Control for Trustworthy Unmanned Aerial Vehicles Based on Factor Graph Optimization in Urban Transportation