Joint localization of pursuit quadcopters and target using monocular cues

Basit, Abdul, Qureshi, Waqar S., Dailey, Matthew N. and Krajník, Tomáš (2015) Joint localization of pursuit quadcopters and target using monocular cues. Journal of Intelligent & Robotic Systems, 78 (3-4). pp. 613-630. ISSN 0921-0296

pursuit_2014_JINT.pdf - Whole Document
Available under License Creative Commons Attribution.

Item Type:Article
Item Status:Live Archive


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, combined noise of the quadcopter's sensors causes large disturbances of 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.

Keywords:Quadcopters, Joint localization, Monocular cues, State estimation filters, Visual tracking, Redetection, Backprojection, Pursuit robot, AR.Drone, bmjgoldcheck, NotOAChecked
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:14890
Deposited On:10 Sep 2014 17:53

Repository Staff Only: item control page