  Today, mobile robots are expected to carry out increasingly complex tasks in
multifarious, real-world environments. Often, the tasks require a certain
semantic understanding of the workspace. Consider, for example, spoken
instructions from a human collaborator referring to objects of interest; the
robot must be able to accurately detect these objects to correctly understand
the instructions. However, existing object detection, while competent, is not
perfect. In particular, the performance of detection algorithms is commonly
sensitive to the position of the sensor relative to the objects in the scene.
This paper presents an online planning algorithm which learns an explicit model
of the spatial dependence of object detection and generates plans which
maximize the expected performance of the detection, and by extension the
overall plan performance. Crucially, the learned sensor model incorporates
spatial correlations between measurements, capturing the fact that successive
measurements taken at the same or nearby locations are not independent. We show
how this sensor model can be incorporated into an efficient forward search
algorithm in the information space of detected objects, allowing the robot to
generate motion plans efficiently. We investigate the performance of our
approach by addressing the tasks of door and text detection in indoor
environments and demonstrate significant improvement in detection performance
during task execution over alternative methods in simulated and real robot
experiments.
