Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields
This work addresses robust vision-based navigation for miniaturized aerial robots with limited computational resources, though it is incremental as it builds on existing potential field methods.
The paper tackles the problem of visual localization failure in mobile robots by proposing a low-level navigation algorithm based on artificial potential fields, which drives the robot toward a goal while favoring feature-rich areas to improve localization, with simulations and real experiments on a mini quadrotor showing effective goal achievement and prevention of localization failure.
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.