| Title |
Local Visual Homing Navigation Using Gradient-Descent Learning of Haar-like Features |
| Authors |
김만동(Man-Dong Kim) ; 김대은(DaeEun Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.10.1244 |
| Keywords |
Local visual navigation; Haar-like features; Visual homing; Gradient-descent method |
| Abstract |
The autonomous mobile technology of mobile robots has been developed. Visual navigation is one of non-trivial problems and it has been tackled with biologically inspired models. Especially; ant navigation system inspires robot navigation. The visual cell structure of ants was modeled with Haar-like features. Those features can be obtained with computationally efficient process. In this paper; we handle visual homing navigation where an agent is supposed to return home after exploration in the environment. We apply a learning process based on gradient-descent algorithm to estimate the homing vector at an arbitrary position of a mobile agent. Our approach is simple but very effective to find the homing vector and its performance is better than the conventional algorithm. From our results; the Haar-like features in the snapshot images are sufficient to estimate the homing vector. |