# DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning

arXiv: https://arxiv.org/abs/1806.07379 · HTML: https://mlsanity.com/paper/1806.07379/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Mars Terrain Segmentation with Less Labels](https://mlsanity.com/paper/2202.00791/) (2022)
- [S5Mars: Semi-Supervised Learning for Mars Semantic Segmentation](https://mlsanity.com/paper/2207.01200/) (2022)
- [Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images](https://mlsanity.com/paper/2204.09854/) (2022)
- [Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments](https://mlsanity.com/paper/2104.01560/) (2021)
- [Enhancing Martian Terrain Recognition with Deep Constrained Clustering](https://mlsanity.com/paper/2503.17633/) (2025)
- [Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data](https://mlsanity.com/paper/2605.27644/) (2026)

## Abstract (arXiv)

Terramechanics plays a critical role in the areas of ground vehicles and ground mobile robots since understanding and estimating the variables influencing the vehicle-terrain interaction may mean the success or the failure of an entire mission. This research applies state-of-the-art algorithms in deep learning to two key problems: estimating wheel slip and classifying the terrain being traversed by a ground robot. Three data sets collected by ground robotic platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot Fitorobot) are employed in order to compare the performance of traditional machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). This work also shows the impact that certain tuning parameters and the network architecture (MLP, DNN and CNN) play on the performance of those methods. This paper also contributes a deep discussion with the lessons learned in the implementation of DNNs and CNNs and how these methods can be extended to solve other problems.