# Proprioception Is All You Need: Terrain Classification for Boreal Forests

arXiv: https://arxiv.org/abs/2403.16877 · HTML: https://mlsanity.com/paper/2403.16877/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 9 of 9)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning](https://mlsanity.com/paper/1912.03227/) (2019)
- [Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification](https://mlsanity.com/paper/1804.00736/) (2018)
- [Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned](https://mlsanity.com/paper/2111.13981/) (2021)
- [Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision](https://mlsanity.com/paper/2005.05175/) (2020)
- [On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation](https://mlsanity.com/paper/2209.03226/) (2022)
- [Terrain assessment for precision agriculture using vehicle dynamic modelling](https://mlsanity.com/paper/2104.06326/) (2021)
- [DRIVE: Data-driven Robot Input Vector Exploration](https://mlsanity.com/paper/2309.10718/) (2023)
- [Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions](https://mlsanity.com/paper/2310.07844/) (2023)

## Built on by (most cited first, 4 of 4)

- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities](https://mlsanity.com/paper/2505.09477/) (2025)
- [Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets](https://mlsanity.com/paper/2606.18698/) (2026)
- [Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification](https://mlsanity.com/paper/2610.04240/) (2026)

## Abstract (arXiv)

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.