Navigation toward non-static target object using footprint detection based tracking

Authors:
Meng Yi;Yinfei Yang;Wenjing Qi;Yu Zhou;Yunfeng Li;Zygmunt Pizlo;Longin Jan Latecki
Affiliations:
Dept. of Computer and Information Science, Temple University, Philadelphia;GRASP Laboratory, University of Pennsylvania;Dept. of Computer and Information Science, Temple University, Philadelphia;Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, China;Dept. of Psychological Sciences, Purdue University;Dept. of Psychological Sciences, Purdue University;Dept. of Computer and Information Science, Temple University, Philadelphia
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Year:
2012

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Abstract

The destination of a traditional robot navigation task is usually a static location. However, many real life applications require a robot to continuously identify and find its way toward a non-static target, e.g., following a walking person. In this paper, we present a navigation framework for this task which is based on simultaneous navigation and tracking. It consists of iterations of data acquiring, perception/cognition and motion executing. In the perception/cognition step, visual tracking is introduced to keep track of the target object. This setting is much more challenging than regular tracking tasks, because the target object shows much larger variance in location, shape and size in consecutive images acquired while navigating. A Footprint Detection based Tracker (FD-Tracker) is proposed to robustly track the target object in such scenarios. We first perform object footprint detection in the plan-view map to grasp possible target locations. The information is then fused into a Bayesian tracking framework to prune target candidates. As compared to previous methods, our results demonstrate that using footprint can boost the performance of visual tracker. Promising experimental results of navigating a robot to various goals in an office environment further proofs the robustness of our navigation framework.