Abstract
Pursuit robots (autonomous robots tasked with tracking and pursuing a moving target) require accurate tracking of the target’s position over time. One possibly effective pursuit platform is a quadcopter equipped with basic sensors and a monocular camera. However, the combined noise in the quadcopter’s sensors causes large disturbances in the target’s 3D position estimate. To solve this problem, in this paper, we propose a novel method for joint localization of a quadcopter pursuer with a monocular camera and an arbitrary target. Our method localizes both the pursuer and target with respect to a common reference frame. The joint localization method fuses the quadcopter’s kinematics and the target’s dynamics in a joint state space model. We show that predicting and correcting pursuer and target trajectories simultaneously produces better results than standard approaches to estimating relative target trajectories in a 3D coordinate system. Our method also comprises a computationally efficient visual tracking method capable of redetecting a temporarily lost target. The efficiency of the proposed method is demonstrated by a series of experiments with a real quadcopter pursuing a human. The results show that the visual tracker can deal effectively with target occlusions and that joint localization outperforms standard localization methods.
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Basit, A., Qureshi, W.S., Dailey, M.N. et al. Joint Localization of Pursuit Quadcopters and Target Using Monocular Cues. J Intell Robot Syst 78, 613–630 (2015). https://doi.org/10.1007/s10846-014-0081-2
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DOI: https://doi.org/10.1007/s10846-014-0081-2