  Accurate robot localization relative to orchard row centerlines is essential
for autonomous guidance where satellite signals are often obstructed by
foliage. Existing sensor-based approaches rely on various features extracted
from images and point clouds. However, any selected features are not available
consistently, because the visual and geometrical characteristics of orchard
rows change drastically when tree types, growth stages, canopy management
practices, seasons, and weather conditions change. In this work, we introduce a
novel localization method that doesn't rely on features; instead, it relies on
the concept of a row-sensing template, which is the expected observation of a
3D sensor traveling in an orchard row, when the sensor is anywhere on the
centerline and perfectly aligned with it. First, the template is built using a
few measurements, provided that the sensor's true pose with respect to the
centerline is available. Then, during navigation, the best pose estimate (and
its confidence) is estimated by maximizing the match between the template and
the sensed point cloud using particle-filtering. The method can adapt to
various orchards and conditions by re-building the template. Experiments were
performed in a vineyard, and in an orchard in different seasons. Results showed
that the lateral mean absolute error (MAE) was less than 3.6% of the row width,
and the heading MAE was less than 1.72 degrees. Localization was robust, as
errors didn't increase when less than 75% of measurement points were missing.
The results indicate that template-based localization can provide a generic
approach for accurate and robust localization in real-world orchards.
