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Robust and effective fruit detection and localization is essential forrobotic harvesting systems. While extensive research efforts have been devotedto improving fruit detection, less emphasis has been placed on the fruitlocalization aspect, which is a crucial yet challenging task due to limiteddepth accuracy from existing sensor measurements in the natural orchardenvironment with variable lighting conditions and foliage/branch occlusions. Inthis paper, we present the system design and calibration of an ActiveLAser-Camera Scanner (ALACS), a novel perception module for robust andhigh-precision fruit localization. The hardware of ALACS mainly consists of ared line laser, an RGB camera, and a linear motion slide, which are seamlesslyintegrated into an active scanning scheme where a dynamic-targetinglaser-triangulation principle is employed. A high-fidelity extrinsic model isdeveloped to pair the laser illumination and the RGB camera, enabling precisedepth computation when the target is captured by both sensors. A random sampleconsensus-based robust calibration scheme is then designed to calibrate themodel parameters based on collected data. Comprehensive evaluations areconducted to validate the system model and calibration scheme. The results showthat the proposed calibration method can detect and remove data outliers toachieve robust parameter computation, and the calibrated ALACS system is ableto achieve high-precision localization with millimeter-level accuracy.

Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration