Terrain Classification with a Reservoir-Based Network of Spiking Neurons

Terrain classification is important for outdoor path planning, mapping, and navigation. We developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types (i.e. grass, dirt, and road) in a botanical garden. It included a recurrent layer and a supervised layer. The input spike trains to the recurrent layer were generated from linear accelerometer and gyroscope sensor signals as well as camera frames from an Android smartphone that controlled a ground robot. Compared to a Support Vector Machine (SVM) model and a 3-layer (3L) logistic regression model, our r-SNN method generated better prediction accuracy without reliance on a time window of data. Using both images and sensors as input, the test accuracy of the r-SNN was over 95%, which was significantly better than the SVM and the 3L logistic regression. Because the r-SNN is compatible with neuromorphic hardware, our proposed method could be part of a biologically-inspired power-efficient autonomous robot navigation system.