Low-Level Active Visual Navigation: Increasing Robustness of Vision-Based Localization Using Potential Fields
Rômulo T. Rodrigues, Meysam Basiri, A. Pedro Aguiar, Pedro Miraldo
Abstract
This letter 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.
BibTeX
@inproceedings{ral2018_lowlevelactivevi,
title = {Low-Level Active Visual Navigation: Increasing Robustness of Vision-Based Localization Using Potential Fields},
author = {Rômulo T. Rodrigues and Meysam Basiri and A. Pedro Aguiar and Pedro Miraldo},
booktitle = {RA-L 2018},
year = {2018}
}