Snapshot Navigation in the Wavelet
Domain
Stefan Meyer
(B) , Thomas Nowotny , Paul Graham , Alex Dewar ,
and Andrew Philippides
University of Sussex, Falmer, Brighton BN1 9RH, UK
s.meyer@sussex.ac.uk
Abstract. Many animals rely on robust visual navigation which can be
explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading
by comparing current views to the stored snapshots. Long-range route
navigation can also be explained by such models, by storing multiple
snapshots along a training route and comparing the current image to
these. For such models, memory capacity and comparison time increase
dramatically with route length, rendering them unfeasible for smallbrained insects and low-power robots where computation and storage
are limited. One way to reduce the requirements is to use a compressed
image representation. Inspired by the filter bank-like arrangement of the
visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By
decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains
robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased
performance over a range of other models.
Keywords: Insect navigation · Visual homing · Wavelet
1 Introduction
Many insects use view-based route following as part of their navigational toolkit
[11,13,35] and it is remarkable that with small brains and low resolution vision
their visual route navigation is so efficient and robust [5]. The first computational
model of insect visual navigation is the snapshot model [4]. This has inspired a
series of view-based models. For example [2,3] have modelled how ants might
store a set of views to guide them on long routes. Here, it is assumed that the ant
stores snapshots experienced during route traversal. When lost, the ant recovers
the right orientation by comparing rotated versions of its current view with all
This work was funded by the EPSRC (grant EP/P006094/1) and a University of Sussex
scholarship.
c
Springer Nature Switzerland AG 2020
V. Vouloutsi et al. (Eds.): Living Machines 2020, LNAI 12413, pp. 245–256, 2020.
https://doi.org/10.1007/978-3-030-64313-3_24
Domain
Stefan Meyer
(B) , Thomas Nowotny , Paul Graham , Alex Dewar ,
and Andrew Philippides
University of Sussex, Falmer, Brighton BN1 9RH, UK
s.meyer@sussex.ac.uk
Abstract. Many animals rely on robust visual navigation which can be
explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading
by comparing current views to the stored snapshots. Long-range route
navigation can also be explained by such models, by storing multiple
snapshots along a training route and comparing the current image to
these. For such models, memory capacity and comparison time increase
dramatically with route length, rendering them unfeasible for smallbrained insects and low-power robots where computation and storage
are limited. One way to reduce the requirements is to use a compressed
image representation. Inspired by the filter bank-like arrangement of the
visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By
decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains
robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased
performance over a range of other models.
Keywords: Insect navigation · Visual homing · Wavelet
1 Introduction
Many insects use view-based route following as part of their navigational toolkit
[11,13,35] and it is remarkable that with small brains and low resolution vision
their visual route navigation is so efficient and robust [5]. The first computational
model of insect visual navigation is the snapshot model [4]. This has inspired a
series of view-based models. For example [2,3] have modelled how ants might
store a set of views to guide them on long routes. Here, it is assumed that the ant
stores snapshots experienced during route traversal. When lost, the ant recovers
the right orientation by comparing rotated versions of its current view with all
This work was funded by the EPSRC (grant EP/P006094/1) and a University of Sussex
scholarship.
c
Springer Nature Switzerland AG 2020
V. Vouloutsi et al. (Eds.): Living Machines 2020, LNAI 12413, pp. 245–256, 2020.
https://doi.org/10.1007/978-3-030-64313-3_24