Recurrent Neural Networks for fast and robust vibration-based ground classification on mobile robots

This paper investigates Recurrent Neural Networks (RNNs), particularly Dynamic Cortex Memories (DCMs), an extension of Long Short Term Memories (LSTMs) for classification of 14 different ground types based on vibration data. Also a simple regularization technique called Sequence Boundary Dropout (SBD) is introduced, which effectively enlarges the training set and improves generalization. The neural networks perform in the time domain without any explicit feature computation, while previous state-of-the-art methods extract features mainly in the frequency domain. The presented approach does not require a time window, is causal, and works just-in-time, such that a classification can be done online at each new time step. Furthermore, we show that the neural networks outperform previous methods significantly in terms of classification accuracy. Finally, we demonstrate that the networks retrained with Random Activation Preservation (RAP) can classify very early - within a fraction of a second - but robustly at the same time in a continuous recognition scenario with varying classes.