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.

翻译：稳健且有效的果实检测与定位对于机器人采摘系统至关重要。尽管已有大量研究致力于改进果实检测，但对果实定位方面的关注相对较少。由于自然果园环境中存在变化的光照条件及枝叶遮挡，现有传感器测量的深度精度有限，因此果实定位是一项关键但具有挑战性的任务。本文介绍了主动式激光-相机扫描仪（ALACS）的系统设计与标定，这是一种用于实现稳健且高精度果实定位的新型感知模块。ALACS的硬件主要由红色线激光器、RGB相机和线性运动滑台组成，这些组件无缝集成到一种采用动态靶向激光三角测量原理的主动扫描方案中。我们建立了一个高保真的外参模型来配对激光照明与RGB相机，从而在目标被两个传感器同时捕捉时实现精确的深度计算。随后设计了一种基于随机采样一致性的稳健标定方案，利用采集的数据标定模型参数。通过综合评估验证了系统模型与标定方案的有效性。结果表明，所提出的标定方法能够检测并去除数据异常值，实现稳健的参数计算；标定后的ALACS系统能够达到毫米级的高精度定位。