LiDAR-based odometry is a cornerstone of autonomous navigation, robotic perception, and mapping, offering precise pose estimation even when cameras or GPS become unreliable. However, most existing algorithms are designed and benchmarked for structured, urban-like environments, leaving their robustness in unstructured settings—such as vineyards, forests, and off-road terrains—insufficiently validated. This gap poses a critical limitation for deploying autonomous systems in agricultural robotics and other non-urban applications where localization failures can compromise safety and mission success. Four state-of-the-art LiDAR odometry algorithms—KISS-ICP, Genz-ICP, MOLA-LO, and SiMpLe—were evaluated on two contrasting datasets. The KITTI benchmark represented structured urban roads, while a custom vineyard dataset collected by the Interdepartmental Center PiC4SER captured the irregular and repetitive geometry of agricultural fields.