Regional Conference on Permafrost 2021 and the 19th International Conference on Cold Regions Engineering
Quantification of Rut Detection and Height Mapping in Winter Terrains for Off-Road Mobility
Publication: Permafrost 2021: Merging Permafrost Science and Cold Regions Engineering
ABSTRACT
Off-road autonomous vehicle navigation in winter environments requires reliable identification and quantification of potential obstacles, such as deep vehicle rutting or buried objects. The advent of consumer-grade light detection and ranging (LiDAR) sensors and unmanned aerial system (UAS) based photogrammetry present new avenues for the implementation of change detection algorithms for the purpose of obstacle identification. Few studies have provided a quantifiable statistical method for determining the input parameters of these change detection algorithms based upon user-defined confidence metrics. Previous detection methods also fail to derive the degree of assurance associated with the identification of a perceived obstacle. Here, we present an automated method for identification of snow-covered obstacles and vehicle ruts within LiDAR-derived digital elevation models based on false-alarm and detection probabilities. Detection maps and accurate height maps are generated for snow-covered objects by the algorithm to demonstrate the reliability of this method to assist with obstacle avoidance in snowy off-road conditions. The algorithm described here is a reliable and fast method for the identification and measurement of snow-covered obstacles. While this study is concerned with snow-covered terrain, the methods described here may be leveraged to monitor route deformation features as a result of vehicle traffic across a variety of terrain types.
Get full access to this article
View all available purchase options and get full access to this chapter.
ACKNOWLEDGEMENTS
This project was funded by the ST CoVER work package (3A6FA5). The authors would like to acknowledge the contributions of Bruce Elder, Alex Stott, and Jason Olivier towards the collection and processing of the data presented within this report.
REFERENCES
Botha, T., Johnson, D., Els, S., Shoop, S. (2019). “Real time rut profile measurement in varying terrain types using digital image correlation.” Journal of Terramechanics., 82, 53–61. https://doi.org/10.1016/j.jterra.2018.12.00
Che, E., Jung, J., Olsen, M. J. (2019). "Object recognition, segmentation, and classification of mobile laser scanning point clouds: A state of the art review." Sensors 19(4), 810.
Fawcett, T. (2006). "An introduction to ROC analysis." Pattern recognition letters., 27(8), pp.861-874.
Johnson, R. A., and D.W. Wichern (2002). Applied multivariate statistical analysis. Vol. 5: Prentice Hall Upper Saddle River, NJ.
Marra, E., Cambi, M., Fernandez-Lacruz, R., Giannetti, F., Marchi, E., Nordfjell, T. (2018). "Photogrammetric estimation of wheel rut dimensions and soil compaction after increasing numbers of forwarder passes." Scandinavian Journal of Forest Research, 33(6), 613–620. https://doi.org/10.1080/02827581.2018.1427789
Meadows, W., Hudson, C., Goodin, C., Dabbiru, L., Powell, B., Doude, M., Carruth, D., Islam, M., Ball, J.E., and Tang, Bo. (2019). "Multi-LiDAR placement, calibration, co-registration, and processing on a Subaru Forester for off-road autonomous vehicles operations." In Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019. vol. 11009, p. 110090J. International Society for Optics and Photonics, 2019.
Nevalainen, P., Salmivaara, A., Ala-Ilomäki, J., Launiainen, S., Hiedanpää, J., Finér, L., Pahikkala, T., Heikkonen, J. (2017). "Estimating the Rut Depth by UAV Photogrammetry." Remote Sensing, 9(12), 1279. https://doi.org/10.3390/rs9121279
Pingel, T. J., Clarke, K. C., McBride, W. A. (2013). "An improved simple morphological filter for the terrain classification of airborne LIDAR data." ISPRS Journal of Photogrammetry and Remote Sensing, 77, 21–30. https://doi.org/10.1016/j.isprsjprs.2012.12.002
Salmivaara, A., Miettinen, M., Finér, L., Launiainen, S., Korpunen, H., Tuominen, S., Heikkonen, J., Nevalainen, P., Sirén, M., Ala-Ilomäki, J., Uusitalo, J. (2018). "Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?". International Journal of Forest Engineering, 29(1), 41–52. https://doi.org/10.1080/14942119.2018.1419677
Scott, D.W. (1982). Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons, New York, Chicester
Silverman, B. W. (1982). Density estimation for statistics and data analysis (Vol. 26). CRC press.
Wand, M.P., and Jones, M.C. (1994). Kernel smoothing. Crc Press.
Zhang, K., Chen, S.C., Whitman, D., Shyu, M.L., Yan, J. and Zhang, C. (2003). "A progressive morphological filter for removing nonground measurements from airborne LIDAR data." IEEE transactions on geoscience and remote sensing, 41(4), pp.872-882.
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., Yan, G. (2016). "An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation." Remote Sensing, 8(6), 501. https://doi.org/10.3390/rs8060501
Information & Authors
Information
Published In
Permafrost 2021: Merging Permafrost Science and Cold Regions Engineering
Pages: 172 - 183
Editor: Jon Zufelt, Ph.D., HDR Alaska
ISBN (Online): 978-0-7844-8358-9
Copyright
© 2021 American Society of Civil Engineers.
History
Published online: Oct 21, 2021
Published in print: Oct 21, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.