Proprioception Is All You Need: Terrain Classification for Boreal
  Forests
Damien LaRocque William Guimont-Martin David-Alexandre Duclos Philippe Giguère François Pomerleau
Recent works in field robotics highlighted the importance of resiliency
against different types of terrains. Boreal forests, in particular, are home to
many mobility-impeding terrains that should be considered for off-road
autonomous navigation. Also, being one of the largest land biomes on Earth,
boreal forests are an area where autonomous vehicles are expected to become
increasingly common. In this paper, we address this issue by introducing
BorealTC, a publicly available dataset for proprioceptive-based terrain
classification (TC). Recorded with a Husky A200, our dataset contains 116 min
of Inertial Measurement Unit (IMU), motor current, and wheel odometry data,
focusing on typical boreal forest terrains, notably snow, ice, and silty loam.
Combining our dataset with another dataset from the state-of-the-art, we
evaluate both a Convolutional Neural Network (CNN) and the novel state space
model (SSM)-based Mamba architecture on a TC task. Interestingly, we show that
while CNN outperforms Mamba on each separate dataset, Mamba achieves greater
accuracy when trained on a combination of both. In addition, we demonstrate
that Mamba's learning capacity is greater than a CNN for increasing amounts of
data. We show that the combination of two TC datasets yields a latent space
that can be interpreted with the properties of the terrains. We also discuss
the implications of merging datasets on classification. Our source code and
dataset are publicly available online:
https://github.com/norlab-ulaval/BorealTC.