  Robust and effective fruit detection and localization is essential for
robotic harvesting systems. While extensive research efforts have been devoted
to improving fruit detection, less emphasis has been placed on the fruit
localization aspect, which is a crucial yet challenging task due to limited
depth accuracy from existing sensor measurements in the natural orchard
environment with variable lighting conditions and foliage/branch occlusions. In
this paper, we present the system design and calibration of an Active
LAser-Camera Scanner (ALACS), a novel perception module for robust and
high-precision fruit localization. The hardware of ALACS mainly consists of a
red line laser, an RGB camera, and a linear motion slide, which are seamlessly
integrated into an active scanning scheme where a dynamic-targeting
laser-triangulation principle is employed. A high-fidelity extrinsic model is
developed to pair the laser illumination and the RGB camera, enabling precise
depth computation when the target is captured by both sensors. A random sample
consensus-based robust calibration scheme is then designed to calibrate the
model parameters based on collected data. Comprehensive evaluations are
conducted to validate the system model and calibration scheme. The results show
that the proposed calibration method can detect and remove data outliers to
achieve robust parameter computation, and the calibrated ALACS system is able
to achieve high-precision localization with millimeter-level accuracy.
