Title: Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields,
Abstract: This paper proposes a low-level visual navigation algorithm to improve visual
localization of a mobile robot. The algorithm, based on artificial potential
fields, associates each feature in the current image frame with an attractive
or neutral potential energy, with the objective of generating a control action
that drives the vehicle towards the goal, while still favoring feature rich
areas within a local scope, thus improving the localization performance. One
key property of the proposed method is that it does not rely on mapping, and
therefore it is a lightweight solution that can be deployed on miniaturized
aerial robots, in which memory and computational power are major constraints.
Simulations and real experimental results using a mini quadrotor equipped with
a downward looking camera demonstrate that the proposed method can effectively
drive the vehicle to a designated goal through a path that prevents
localization failure.