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.
- 4 Trajectories of more than 2 km were used to train and validate 5 kinematic models for SSMRs on dry concrete and snow-covered terrain
- A Clearpath Robotics Warthog unmanned ground vehicle was used
- An extensive analysis of model parameter training is given in the results
- Our paper is available here
- It was presented at the 17th Conference on Computer and Robot Vision (CRV)
Baril, D., Grondin, V., Deschênes, S., Laconte, J., M., V., Kubelka, V., Gallant, A., Giguère, P., & Pomerleau, F. (2020). Evaluation of Skid-Steering Kinematic Models for Subarctic Environments. Proceeding of the 2020 17th Conference on Computer and Robot Vision (CRV).