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.

为了解决在北方森林进行越野自主导航所面临的地形分类问题，本文通过引入BorealTC数据集，结合另一个数据集进行评估，分别采用卷积神经网络（CNN）和基于状态空间模型的Mamba架构，研究了传感器数据的地形分类任务。研究发现，CNN在各自的数据集上表现更好，而基于Mamba架构的模型在结合两个数据集后的综合准确性更高，并且对于增加的数据量，Mamba的学习能力较CNN更强。研究还证明了将两个地形分类数据集相结合后产生的潜在空间可以解释地形特性，并讨论了数据集合并对分类结果的影响。

仅需本体感知：北方森林的地形分类