Multi-target Association and Localization with Distributed Drone Following: A Factor Graph Approach

Abstract

Vision-based multi-drone multi-object tracking technology enables autonomous target situational awareness for unmanned aerial systems. Distributed observer drones dynamically estimate the spatio-temporal states of multiple targets through collaborative sensor fusion, enabling simultaneous localization and persistent following of the target of interest in cluttered airspaces. The challenge lies in distinguishing targets in different drones’ views and keeping the target of interest within the field of view. This paper proposes a factor graph method for joint multi-target association and localization with distributed drone following. Sensor measurements and control constraints are integrated into a probabilistic factor graph to solve the bundle adjustment and model predictive control, respectively. Both simulation and real-world experiments prove the effectiveness and robustness of our proposed approach. The source code will be available at: https://github.com/npu-ius-lab/MLMF.