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Accurate robot localization relative to orchard row centerlines is essentialfor autonomous guidance where satellite signals are often obstructed byfoliage. Existing sensor-based approaches rely on various features extractedfrom images and point clouds. However, any selected features are not availableconsistently, because the visual and geometrical characteristics of orchardrows change drastically when tree types, growth stages, canopy managementpractices, seasons, and weather conditions change. In this work, we introduce anovel localization method that doesn't rely on features; instead, it relies onthe concept of a row-sensing template, which is the expected observation of a3D sensor traveling in an orchard row, when the sensor is anywhere on thecenterline and perfectly aligned with it. First, the template is built using afew measurements, provided that the sensor's true pose with respect to thecenterline is available. Then, during navigation, the best pose estimate (andits confidence) is estimated by maximizing the match between the template andthe sensed point cloud using particle-filtering. The method can adapt tovarious orchards and conditions by re-building the template. Experiments wereperformed in a vineyard, and in an orchard in different seasons. Results showedthat 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, aserrors didn't increase when less than 75% of measurement points were missing.The results indicate that template-based localization can provide a genericapproach for accurate and robust localization in real-world orchards.

Row-sensing Templates: A Generic 3D Sensor-based Approach to Robot Localization with Respect to Orchard Row Centerlines