Snapshot Navigation in the Wavelet Domain

Meyer, Stefan; Nowotny, Thomas; Graham, Paul; Dewar, Alex; Philippides, Andrew

doi:10.1007/978-3-030-64313-3_24

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…To compensate for this, several image processing methods have been proposed. While some methods are similar to feature detection, as with skyline (Graham & Cheng, 2009), landmark (Möller et al, 1999) or Haar-like features (Baddeley et al, 2011), others take a holistic frequency filtering approach (Stone et al, 2018;Meyer et al, 2020). More computationally intensive methods like object recognition could be employed, albeit probably for simple shapes given low resolution insect vision.…”

Section: Discussionmentioning

confidence: 99%

Familiarity-taxis: A bilateral approach to view-based navigation

Steinbeck,

Kagioulis,

Dewar

et al. 2023

Preprint

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Many insects use view-based navigation, or snapshot matching, to return to familiar locations, or navigate routes. This relies on egocentric memories being matched to current views of the world. Previous snapshot navigation algorithms have used full panoramic vision for the comparison of memorised images with query images to establish a measure of familiarity, which leads to a recovery of the original heading direction from when the snapshot was taken. Many aspects of insect sensory systems are lateralised with steering being derived from the comparison of left and right signals like a classic Braitenberg vehicle. Here we investigate whether view-based route navigation can be implemented using bilateral visual familiarity comparisons. We found that the difference in familiarity between estimates from left and right fields of view can be used as a steering signal to recover the original heading direction. This finding extends across many different sizes of field of view and visual resolutions. In insects, steering computations are implemented in a brain region called the Lateral Accessory Lobe, within the Central Complex. In a simple simulation we show with a SNN model of the LAL an existence proof of how bilateral visual familiarity could drive a search for a visually defined goal.

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Section: Discussionmentioning

confidence: 99%

Familiarity-taxis: A bilateral approach to view-based navigation

Steinbeck,

Kagioulis,

Dewar

et al. 2023

Preprint

Self Cite

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show abstract

“…We have not as yet exploited the multi-resolution aspect of our wavelet function since adding finer levels added computational burden. As discussed in [39], weighting coefficients at different scales could provide more scene recognition discriminability. In initial trials, we found that adding the higher resolution frequency bands tended to reduce the catchment area of reference images.…”

Section: Wavelet-based Image Matchingmentioning

confidence: 99%

“…In recent years, studies have shown that Haar like wavelet features can improve view discrimination in the task of visual homing while reducing memory requirements [31,39]. Here, we adopt a bandpass filtering approach, using a computational method that was originally inspired by the orientated bandpass filter properties of V1 neurons in vertebrates [48].…”

Section: Introductionmentioning

confidence: 99%

Looking down: a model for visual route following in flying insects

Stankiewicz

Webb

2021

Bioinspir. Biomim.

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Insect visual navigation is often assumed to depend on panoramic views of the horizon, and how these change as the animal moves. However, it is known that honey bees can visually navigate in flat, open meadows where visual information at the horizon is minimal, or would remain relatively constant across a wide range of positions. In this paper we hypothesise that these animals can navigate using view memories of the ground. We find that in natural scenes, low resolution views from an aerial perspective of ostensibly self-similar terrain (e.g. within a field of grass) provide surprisingly robust descriptors of precise spatial locations. We propose a new visual route following approach that makes use of transverse oscillations to centre a flight path along a sequence of learned views of the ground. We deploy this model on an autonomous quadcopter and demonstrate that it provides robust performance in the real world on journeys of up to 30 m. The success of our method is contingent on a robust view matching process which can evaluate the familiarity of a view with a degree of translational invariance. We show that a previously developed wavelet based bandpass orientated filter approach fits these requirements well, exhibiting double the catchment area of standard approaches. Using a realistic simulation package, we evaluate the robustness of our approach to variations in heading direction and aircraft height between inbound and outbound journeys. We also demonstrate that our approach can operate using a vision system with a biologically relevant visual acuity and viewing direction.

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Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

et al. 2021

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This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies.

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Snapshot Navigation in the Wavelet Domain

Cited by 4 publications

References 39 publications

Familiarity-taxis: A bilateral approach to view-based navigation

Familiarity-taxis: A bilateral approach to view-based navigation

Looking down: a model for visual route following in flying insects

Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

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