Description:
Inspired by the learning walks of the ant Ocymyrmex robustior, the original multi-snapshot model was introduced, which—in contrast to the classical “single snapshot at the goal” model—collects multiple snapshots in the vicinity of the goal location that subsequently can be used for homing, that is, for guiding the return to the goal. In this study, we show that the multi-snapshot model can be generalized to homing in three dimensions. In addition to capturing snapshots at positions shifted in all three dimensions, we suggest to decouple the home direction from the orientation of snapshots and to associate a home vector with each snapshot. We then propose a modification of the multi-snapshot model for three-dimensional route following and evaluate its performance in an accurate reconstruction of a real environment. As an illumination-invariant alternative to grayscale images, we also examine sky-segmented images. We use spherical harmonics as efficient representation of panoramic images enabling low memory usage and fast similarity estimation of rotated images. The results show that our approach can steer an agent reliably along a route, making it also suitable for robotic applications using on-board computers with limited resources.