Metrological Characterization and Comparison of D415, D455, L515 RealSense Devices in the Close Range

Servi, Michaela; Mussi, Elisa; Profili, Andrea; Furferi, Rocco; Volpe, Yary; Governi, Lapo

doi:10.3390/s21227770

Cited by 29 publications

(15 citation statements)

References 26 publications

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“…This work could be developed in several other future directions. First, we could revisit this study with higher resolution Depth sensors [26] to investigate how the reduction of noise and improvement of resolution in Depth could help to further improve the classification results. More advanced stages of development yet still accessible from the top view, could be investigated without targeting 3D reconstruction [55].…”

Section: Discussionmentioning

confidence: 99%

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Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning

Garbouge

Rasti

Rousseau

2021

Sensors

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The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of 5% correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.

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Section: Discussionmentioning

confidence: 99%

“…Low-cost RGB-Depth cameras are also logically coming with artifacts and noise. Such artifacts and metrological limitations of low-cost RGB-Depth cameras have been extensively studied (see [26] for a recent survey). In our case, we rather work at an informational level.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning

Garbouge

Rasti

Rousseau

2021

Sensors

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“…Servi et al proposed a method for testing the effects of sampling distance to the imaging accuracy using different Realsense cameras, with results that illustrated that the farther the working distance is, the worse the results in image sampling. The structuredlight measurement method also predetermines the working distance and condition of the target surface, which are two important influencing factors [34].…”

Section: Hand-eye Coordination Control Strategymentioning

confidence: 99%

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

Wang

Lin

et al. 2022

Agronomy

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For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long–close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots.

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“…Work [29] is devoted to the use of the Intel Real Sense Depth Camera D435 for estimating the distance from the robot to obstacles when constructing a trajectory. Finally, in [30], a comparative analysis of the characteristics of the Real Sense camera line is presented, and in [31], a comparative analysis of depth cameras with laser scanning technologies is performed, showing the sensitivity of both approaches to noise.…”

Section: Related Workmentioning

confidence: 99%

Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

et al. 2022

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Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.

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Metrological Characterization and Comparison of D415, D455, L515 RealSense Devices in the Close Range

Cited by 29 publications

References 26 publications

Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning

Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

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