Robot deployment feasibility study: Left: Real-time deployment of our Modular SNN in a small indoor environment on a CPU. First, the reference dataset is collected and the Modular SNN is trained offline. During inference, the robot moves through the environment, collecting images, and predicting their place labels in real time. The reference and query images are collected at different times of the day. The images were collected at 1 Hz with the robot moving at approximately 1 m/s. The blue line and points represent the reference path and images, while the green line represents the query path. Correct predictions are marked with green crosses, and incorrect predictions are marked with red crosses. The four samples of the query and predicted places show the original images and their preprocessed forms, which are used as input to our Modular SNN. Right: Proof-of-concept robot deployment testing platform. We used an AgileX Scout Mini [60] robot equipped with an Intel RealSense D435 camera.

Researchers from Queensland University of Technology present an energy-efficient #placerecognition system leveraging Spiking Neural Networks with modularity and sequence matching to rival traditional deep networks
ieeexplore.ieee.org/document/107...

#SpikingNeuralNetworks #RobotPerception