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  3. Locs-net: Localizing Convolutional Spiking Neural Network For Fast Visual Place Recognition.
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  3. Locs-net: Localizing Convolutional Spiking Neural Network For Fast Visual Place Recognition.

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

Ugur Akcal1,2,3, Ivan Georgiev Raikov4, Ekaterina Dmitrievna Gribkova3,5

  • 1The Grainger College of Engineering, Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an efficient spiking neural network (SNN) for visual place recognition (VPR), significantly improving performance and reducing computational costs for robotics applications. The novel training method enables SNNs to outperform current state-of-the-art methods on challenging datasets.

Keywords:
convolutional networkslocalizationroboticsspiking neural networkssupervised learningvisual place recognition

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Area of Science:

  • Robotics and Artificial Intelligence
  • Neuromorphic Engineering
  • Computer Vision

Background:

  • Visual place recognition (VPR) is crucial for robot navigation but faces challenges like perceptual aliasing and dynamic scenes.
  • Current artificial neural network (ANN) based VPR methods are computationally inefficient.
  • Spiking neural networks (SNNs) offer potential computational efficiency but face training and real-time performance issues.

Purpose of the Study:

  • To develop an efficient and tractable end-to-end convolutional SNN model for VPR.
  • To improve the training and inference performance of SNNs for VPR tasks.
  • To demonstrate the real-world deployment capabilities of SNNs for VPR on neuromorphic hardware.

Main Methods:

  • Developed an end-to-end convolutional SNN model for VPR using backpropagation for training.
  • Employed rate-based approximations of leaky integrate-and-fire (LIF) neurons during training, switching to spiking LIF neurons for inference.
  • Utilized an ANN-to-SNN conversion strategy for on-chip deployment on neuromorphic hardware (Intel Kapoho Bay).

    • Main Results:

    • Achieved superior performance on Nordland (78.6% precision at 100% recall) and Oxford RobotCar (45.7%) datasets compared to SOTA SNNs.
    • Demonstrated significant improvements in training and inference times.
    • On-chip performance on neuromorphic hardware showed continued outperformance over SNN counterparts, with notable energy efficiency.

    Conclusions:

    • The proposed SNN model offers a simpler training pipeline and enhanced performance for VPR.
    • The approach facilitates rapid prototyping and real-world deployment of SNN-based VPR solutions.
    • This work represents a significant advancement towards prevalent SNN-based robotics solutions.