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With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Multi-object tracking (MOT) algorithms can be categorized between two-stage and single-stage methods. Two-stage methods tend to be simpler to adapt and implement to custom applications, while single-stage methods present a more complex end-to-end tracking method that can yield better results in occluded situations at the cost of more training data. The potential advantages of single-stage methods over two-stage methods depends on the complexity of the sequence of viewpoints that a robot needs to process. In this work, we compare a 3D two-stage MOT algorithm, 3D-SORT, against a 3D single-stage MOT algorithm, MOT-DETR, in three different types of sequences with varying levels of complexity. The sequences represent simpler and more complex motions that a robot arm can perform in a tomato greenhouse. Our experiments in a tomato greenhouse show that the single-stage algorithm consistently yields better tracking accuracy, especially in the more challenging sequences where objects are fully occluded or non-visible during several viewpoints., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

In the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Most multi-object tracking (MOT) algorithms are designed for high frame rate sequences and struggle with the occlusions generated by robots' motions and 3D environments. In this paper, we introduce MOT-DETR, a novel approach to detect and track objects in 3D over time using a combination of convolutional networks and transformers. Our method processes 2D and 3D data, and employs a transformer architecture to perform data fusion. We show that MOT-DETR outperforms state-of-the-art multi-object tracking methods. Furthermore, we prove that MOT-DETR can leverage 3D data to deal with long-term occlusions and large frame-to-frame distances better than state-of-the-art methods. Finally, we show how our method is resilient to camera pose noise that can affect the accuracy of point clouds. The implementation of MOT-DETR can be found here: https://github.com/drapado/mot-detr

Greenhouse production of fruits and vegetables in developed countries is challenged by labor 12 scarcity and high labor costs. Robots offer a good solution for sustainable and cost-effective 13 production. Acquiring accurate spatial information about relevant plant parts is vital for 14 successful robot operation. Robot perception in greenhouses is challenging due to variations in 15 plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based 16 method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which 17 provides essential information to harvest the tomato bunches. 18 19 Specifically, this paper proposes a method that detects four anatomical landmarks in the color 20 image and then integrates 3D point-cloud information to determine the 3D pose. A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method. The results showed: (1) high accuracy in object 23 detection, achieving an Average Precision (AP) of AP@0.5=0.96; (2) an average Percentage of 24 Detected Joints (PDJ) of the keypoints of PhDJ@0.2=94.31%; and (3) 3D pose estimation 25 accuracy with mean absolute errors (MAE) of 11.38o and 9.93o for the relative upper and lower 26 angles between the peduncle and main stem, respectively. Furthermore, the capability to handle 27 variations in viewpoint was investigated, demonstrating the method was robust to view changes. 28 However, canonical and higher views resulted in slightly higher performance compared to other 29 views. Although tomato was selected as a use case, the proposed method is also applicable to 30 other greenhouse crops like pepper., Comment: 26 pages, 8 figures, 7 tables. Agricultural Biosystems Engineering Group, Department of Plant Sciences, 7 Wageningen University and Research, P.O. Box 16, Wageningen, 6700AA, 8 the Netherlands

The agro-food industry is turning to robots to address the challenge of labour shortage. However, agro-food environments pose difficulties for robots due to high variation and occlusions. In the presence of these challenges, accurate world models, with information about object location, shape, and properties, are crucial for robots to perform tasks accurately. Building such models is challenging due to the complex and unique nature of agro-food environments, and errors in the model can lead to task execution issues. In this paper, MinkSORT, a novel method for generating tracking features using a 3D sparse convolutional network in a deepSORT-like approach, is proposed to improve the accuracy of world models in agro-food environments. MinkSORT was evaluated using real-world data collected in a tomato greenhouse, where it significantly improved the performance of a baseline model that tracks tomato positions in 3D using a Kalman filter and Mahalanobis distance. MinkSORT improved the HOTA from 42.8% to 44.77%, the association accuracy from 32.55% to 35.55%, and the MOTA from 57.63% to 58.81%. Different contrastive loss functions for training MinkSORT were also evaluated, and it was demonstrated that it leads to improved performance in terms of three separate precision and recall detection outcomes. The proposed method improves world model accuracy, enabling robots to perform tasks such as harvesting and plant maintenance with greater efficiency and accuracy, which is essential for meeting the growing demand for food in a sustainable manner.