Multiple Sensor Synchronization with theRealSense RGB-D Camera

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

Section: Related Workmentioning

confidence: 99%

Section: The Camera Synchronization Systemmentioning

confidence: 99%

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows

Lee,

Alam

et al. 2024

“…With the results and analysis we obtained from thorough experiments, we assist with the implementation of the active stereo setup. Using these analyses and results, many applications of active stereo matching techniques [35][36][37][38][39][40] will be benefited by using the appropriate combination of matching cost, image filter, and stereo algorithms.…”

Section: Number Of Pattern Dotsmentioning

confidence: 99%

A Comparison and Evaluation of Stereo Matching on Active Stereo Images

Jang

Yoon

Lee

et al. 2022

Self Cite

The relationship between the disparity and depth information of corresponding pixels is inversely proportional. Thus, in order to accurately estimate depth from stereo vision, it is important to obtain accurate disparity maps, which encode the difference between horizontal coordinates of corresponding image points. Stereo vision can be classified as either passive or active. Active stereo vision generates pattern texture, which passive stereo vision does not have, on the image to fill the textureless regions. In passive stereo vision, many surveys have discovered that disparity accuracy is heavily reliant on attributes, such as radiometric variation and color variation, and have found the best-performing conditions. However, in active stereo matching, the accuracy of the disparity map is influenced not only by those affecting the passive stereo technique, but also by the attributes of the generated pattern textures. Therefore, in this paper, we analyze and evaluate the relationship between the performance of the active stereo technique and the attributes of pattern texture. When evaluating, experiments are conducted under various settings, such as changing the pattern intensity, pattern contrast, number of pattern dots, and global gain, that may affect the overall performance of the active stereo matching technique. Through this evaluation, our discovery can act as a noteworthy reference for constructing an active stereo system.

“…The procedures are discussed in the following order: (A) RGB-D camera calibration, (B) projector intensity, (C) stereo matching algorithm, (D) 3D reconstruction, (E) outlier removal, and (F) color mapping. Before capturing objects, multiple sensors must be synchronized using an external trigger to capture RGB-D images from the sensors simultaneously [24]. The camera calibration techniques estimate the camera's intrinsic and extrinsic parameters for generating 3D points in the camera's local coordinates (from depth maps) and transform them into points in the global coordinates.…”

Section: D Reconstruction Framework With Multi-view Active Stereo Sen...mentioning

confidence: 99%

Technical Consideration towards Robust 3D Reconstruction with Multi-View Active Stereo Sensors

Jang

Lee

Kang

et al. 2022

Self Cite

It is possible to construct cost-efficient three-dimensional (3D) or four-dimensional (4D) scanning systems using multiple affordable off-the-shelf RGB-D sensors to produce high-quality reconstructions of 3D objects. However, the quality of these systems’ reconstructions is sensitive to a number of factors in reconstruction pipelines, such as multi-view calibration, depth estimation, 3D reconstruction, and color mapping accuracy, because the successive pipelines to reconstruct 3D meshes from multiple active stereo sensors are strongly correlated with each other. This paper categorizes the pipelines into sub-procedures and analyze various factors that can significantly affect reconstruction quality. Thus, this paper provides analytical and practical guidelines for high-quality 3D reconstructions with off-the-shelf sensors. For each sub-procedure, this paper shows comparisons and evaluations of several methods using data captured by 18 RGB-D sensors and provide analyses and discussions towards robust 3D reconstruction. Through various experiments, it has been demonstrated that significantly more accurate 3D scans can be obtained with the considerations along the pipelines. We believe our analyses, benchmarks, and guidelines will help anyone build their own studio and their further research for 3D reconstruction.