Abstract
Efficient encoding of visual information is essential to the success of vision-based navigation tasks in large-scale environments. To do so, we propose in this article the Sparse Max-Pi neural network (SMP), a novel compute-efficient model of visual localization based on sparse and topological encoding of visual information. Inspired by the spatial cognition of mammals, the model uses a “topologic sparse dictionary” to efficiently compress the visual information of a landmark, allowing rich visual information to be represented with very small codes. This descriptor, inspired by the neurons in the primary visual cortex (V1), are learned using sparse coding, homeostasis and self-organising map mechanisms. Evaluated in cross-validation on the Oxford-car dataset, our experimental results show that the SMP model is competitive with the state of the art. It thus provides comparable or better performance than CoHog and NetVlad, two state-of-the-art VPR models.
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Notes
- 1.
Absolute orientation can be obtained from a magnetic or visual compass [2].
- 2.
The model must compare the current image with all images stored in memory to localize a place. The smaller the code, the faster the memory search.
- 3.
To facilitate the notation of the dictionary configuration used during an experiment, the SMP model using a dictionary of size \(n*n\) is called \(SMP-n\).
- 4.
The NetVlad model run at an average frequency of 0.05 Hz with one CPU.
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Colomer, S., Cuperlier, N., Bresson, G., Pechberti, S., Romain, O. (2022). Sparse and Topological Coding for Visual Localization of Autonomous Vehicles. In: Cañamero, L., Gaussier, P., Wilson, M., Boucenna, S., Cuperlier, N. (eds) From Animals to Animats 16. SAB 2022. Lecture Notes in Computer Science(), vol 13499. Springer, Cham. https://doi.org/10.1007/978-3-031-16770-6_13
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