# Evaluation of Skid-Steering Kinematic Models for Subarctic Environments

arXiv: https://arxiv.org/abs/2004.05131 · HTML: https://mlsanity.com/paper/2004.05131/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots](https://mlsanity.com/paper/2404.02515/) (2024)
- [DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty](https://mlsanity.com/paper/2506.16593/) (2025)
- [Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation](https://mlsanity.com/paper/2506.21732/) (2025)

## Abstract (arXiv)

In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-the-art odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-of-curvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models.