Recent advancements in deep learning-based approaches have led to remarkable progress in fruit detection, enabling robust fruit identification in complex environments. However, much less progress has been made on fruit 3D localization, which is equally crucial for robotic harvesting. Complex fruit shape/orientation, fruit clustering, varying lighting conditions, and occlusions by leaves and branches have greatly restricted existing sensors from achieving accurate fruit localization in the natural orchard environment. In this paper, we report on the design of a novel localization technique, called Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D localization. The ALACS hardware setup comprises a red line laser, an RGB color camera, a linear motion slide, and an external RGB-D camera. Leveraging the principles of dynamic-targeting laser-triangulation, ALACS enables precise transformation of the projected 2D laser line from the surface of apples to the 3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction (LLE) method is proposed for robust and high-precision feature extraction on apples. Comprehensive evaluations of LLE demonstrated its ability to extract precise patterns under variable lighting and occlusion conditions. The ALACS system achieved average apple localization accuracies of 6.9 11.2 mm at distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial RealSense RGB-D camera, in an indoor experiment. Orchard evaluations demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71% rate by the RealSense camera. By overcoming the challenges of apple 3D localization, this research contributes to the advancement of robotic fruit harvesting technology.

翻译：近年来，基于深度学习的方法在水果检测领域取得了显著进展，能够在复杂环境中实现鲁棒的水果识别。然而，在同样对机器人采摘至关重要的水果三维定位方面，相关进展则较为有限。水果复杂的形状/朝向、果实簇生、光照条件变化以及叶片和枝条的遮挡，严重制约了现有传感器在自然果园环境中实现精确的水果定位。本文报告了一种名为主动激光-相机扫描（ALACS）的新型定位技术的设计，旨在实现精确且鲁棒的水果三维定位。ALACS硬件系统由一个红色线激光器、一台RGB彩色相机、一个线性运动滑台以及一台外部RGB-D相机组成。利用动态目标激光三角测量原理，ALACS能够将苹果表面投射的二维激光线精确转换为三维空间位置。为便于获取激光图案，本文提出了一种激光线提取（LLE）方法，用于在苹果上实现鲁棒且高精度的特征提取。对LLE的全面评估表明，该方法能够在光照变化及遮挡条件下提取精确的图案。在室内实验中，ALACS系统在1.0米至1.6米距离范围内的平均苹果定位精度达到6.9至11.2毫米，而商用RealSense RGB-D相机的精度为21.5毫米。果园评估表明，ALACS实现了95%的水果摘取成功率，而RealSense相机为71%。通过克服苹果三维定位的挑战，本研究推动了机器人水果采摘技术的进步。