Key points are not available for this paper at this time.
This paper presents a variable resolution framework for autonomously searching stationary targets in a bounded area. Theoretical formulations are also described for using a probabilistic quadtree data structure, which incorporates imperfect Bayesian (false positive and false negative) detections and informs the searcher's route based on optimizing information gain. Live‐fly field experimentation results using a quadrotor unmanned aerial vehicle validate the proposed methodologies and demonstrate an integrated system with autonomous control and embedded object detection for probabilistic search in realistic operational settings. Lessons learned from these field trials include characterization of altitude‐dependent detection performance, and we also present a benchmark data set of outdoor aerial imagery for search and detection applications.
Carpin et al. (Mon,) studied this question.