Estimation of the height profile of the path for autonomous driving in terrain

Tabish Badar*, Issouf Ouattara, Juha Backman, Arto Visala

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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A priori knowledge about the height profile of the path is vital for rollover avoidance in the context of autonomous driving through uneven forest ground. The forest ground is usually covered with either soft vegetation in summertime, or by snow in winter. Thus, the exact solid form of the forest ground cannot be detected by camera or LiDAR. This article, we propose height-odometry and aided height-odometry methods for ground height estimation. The height-odometry method depends solely on interoceptive and proprioceptive sensor data, while the aided height-odometry combines height-odometry output with the existing 3D map information. Thus, the central idea is to build a reference 3D path for autonomous forest machines where the spatial positioning – based on the RTK-GNSS or Forest SLAM method – is fused with the output of (aided) height-odometry method(s). We evaluate the proposed height-odometry methods in two separate environments that are accurately (3D) mapped by a UAV using the advanced machine-vision-based SfM method and the LiDAR-based SLAM algorithms. Through comprehensive data analysis, we demonstrate that the proposed 3D path estimation methods are practical and simple to implement, yet sufficient to estimate the height profile of the path with desired accuracy.

Original languageEnglish
Article number108806
Number of pages10
JournalComputers and Electronics in Agriculture
Publication statusPublished - Apr 2024
MoE publication typeA1 Journal article-refereed


  • 3D elevation model
  • Autonomous ground vehicles
  • Drone mapping
  • Forest machines
  • Simultaneous localization and mapping


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