Key Point-based Orientation Estimation of Strawberries for Robotic Fruit Picking

• URL: http://arxiv.org/abs/2310.11333v1
• Date: Tue, 17 Oct 2023 15:12:11 GMT
• Title: Key Point-based Orientation Estimation of Strawberries for Robotic Fruit Picking
• Authors: Justin Le Lou\"edec and Grzegorz Cielniak
• Abstract summary: We introduce a novel key-point-based fruit orientation estimation method allowing for the prediction of 3D orientation from 2D images directly. Our proposed method achieves state-of-the-art performance with an average error as low as $8circ$, improving predictions by $sim30%$ compared to previous work presented incitewagnerefficient.
• Score: 8.657107511095242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Selective robotic harvesting is a promising technological solution to address labour shortages which are affecting modern agriculture in many parts of the world. For an accurate and efficient picking process, a robotic harvester requires the precise location and orientation of the fruit to effectively plan the trajectory of the end effector. The current methods for estimating fruit orientation employ either complete 3D information which typically requires registration from multiple views or rely on fully-supervised learning techniques, which require difficult-to-obtain manual annotation of the reference orientation. In this paper, we introduce a novel key-point-based fruit orientation estimation method allowing for the prediction of 3D orientation from 2D images directly. The proposed technique can work without full 3D orientation annotations but can also exploit such information for improved accuracy. We evaluate our work on two separate datasets of strawberry images obtained from real-world data collection scenarios. Our proposed method achieves state-of-the-art performance with an average error as low as $8^{\circ}$, improving predictions by $\sim30\%$ compared to previous work presented in~\cite{wagner2021efficient}. Furthermore, our method is suited for real-time robotic applications with fast inference times of $\sim30$ms.

Related papers

• Precise Apple Detection and Localization in Orchards using YOLOv5 for Robotic Harvesting Systems [0.0]
  We propose a novel approach to apple detection and position estimation utilizing an object detection model, YOLOv5. Our results demonstrate that the YOLOv5 model outperforms its counterparts, achieving an impressive apple detection accuracy of approximately 85%.
  arXiv  Detail & Related papers  (2024-05-10T06:17:00Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Improving Online Lane Graph Extraction by Object-Lane Clustering [106.71926896061686]
  We propose an architecture and loss formulation to improve the accuracy of local lane graph estimates. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers. We show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods.
  arXiv  Detail & Related papers  (2023-07-20T15:21:28Z)
• Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots [33.21287030243106]
  Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. We address the problem of jointly estimating complete 3D shapes of fruit and their pose in a 3D multi-resolution map built by a mobile robot.
  arXiv  Detail & Related papers  (2023-03-15T20:41:24Z)
• Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer [26.21320177775571]
  We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable.
  arXiv  Detail & Related papers  (2023-02-28T05:55:42Z)
• Deep Projective Rotation Estimation through Relative Supervision [31.05330535795121]
  Deep learning has offered a way to develop image-based orientation estimators. These estimators often require training on a large labeled dataset. We propose a new algorithm for selfsupervised orientation estimation.
  arXiv  Detail & Related papers  (2022-11-21T04:58:07Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Facilitated machine learning for image-based fruit quality assessment in developing countries [68.8204255655161]
  Automated image classification is a common task for supervised machine learning in food science. We propose an alternative method based on pre-trained vision transformers (ViTs) It can be easily implemented with limited resources on a standard device.
  arXiv  Detail & Related papers  (2022-07-10T19:52:20Z)
• Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking [98.5984733963713]
  We propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. We establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data.
  arXiv  Detail & Related papers  (2022-04-14T15:54:01Z)
• Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards [6.963582954232132]
  geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation. We implement a global-to-local scanning strategy, which enables robots to accurately recognise and retrieve fruits in field environments. Overall, the robotic system achieves success rate of harvesting ranging from 70% - 85% in field harvesting experiments.
  arXiv  Detail & Related papers  (2021-12-08T16:17:26Z)
• AutoSimulate: (Quickly) Learning Synthetic Data Generation [70.82315853981838]
  We propose an efficient alternative for optimal synthetic data generation based on a novel differentiable approximation of the objective. We demonstrate that the proposed method finds the optimal data distribution faster (up to $50times$), with significantly reduced training data generation (up to $30times$) and better accuracy ($+8.7%$) on real-world test datasets than previous methods.
  arXiv  Detail & Related papers  (2020-08-16T11:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.