LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition.

ABSTRACT: Visual place recognition (VPR) is the ability to recognize locations in a physical environment based only on visual inputs. It is a challenging task due to perceptual aliasing, viewpoint and appearance variations and complexity of dynamic scenes. Despite promising demonstrations, many state-of-the-art (SOTA) VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. However, spiking neural networks (SNNs) implemented on neuromorphic hardware are reported to have remarkable potential for more efficient solutions computationally. Still, training SOTA SNNs for VPR is often intractable on large and diverse datasets, and they typically demonstrate poor real-time operation performance. To address these shortcomings, we developed an end-to-end convolutional

SUBMITTER: Akcal U 

PROVIDER: S-EPMC11813887 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature