Emerald – a 16-rotor multicopter for stereo imaging

Purpose The current manuscript aims to propose a novel multirotor design. Design/methodology/approach This paper presents a novel 16-rotor multicopter design named Emerald. The novel design innovations and benefits are disclosed. Comparison to existing 16-rotor designs is carried out. Implementation areas where the novel idea shall yield benefit are discussed. A prototype of the presented design is described. Findings The herein proposed 16-rotor design has a number of benefits over existing 16-rotor multicopters. The paper elaborates on those advantages. Research limitations/implications The research was limited to prototype testing, as the presented design is a novel concept. Practical implications The motivation to research and develop this novel design is implementing the vehicle for stereoscopic photography and reconnaissance. The design is also applicable to carrying payloads while flying indoors.

Panoramic Stereo Imaging of a Bionic Compound-Eye Based on Binocular Vision

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

Diurnal Observations of Wildfires Boundary Layer Dynamics and Aerosol Plume Convection using Stereo-Imaging Techniques

<p>Wildfires have posed increasing risks to human health and loss of life and property. Observations of wildfire remain limited, particularly the plume variables such as injection height and wind velocity critical to assessing wildfire impacts. Lack of adequate spatiotemporal coverage and measurement accuracy hinder predictability and initialization needed by weather and chemical transport models. The new observations from the emerging stereo wind and aerosol imaging techniques with LEO-GEO and GEO-GEO satellites offer an unprecedented opportunity to study wildfire dynamics and evolution processes in great detail. The diurnal coverage of the GEO-GEO winds stereo products (Carr et al., 2020, 2019, 2018) and the daytime coverage (and detail) of GEO multi-angle aerosol products (Limbacher et al., 2021; In Prep) can capture and further our understanding of intense wildfire dynamics (e.g., pyroCb), planetary boundary layer (PBL) variations, and direction of aerosol loadings. Using two new satellite-based stereoscopic tracking algorithms, we compare stereo observations directly with the Coupled WRF-CMAQ simulations (Zou et al., 2019) to diagnose the modeled plume injection height and wind velocity, and aerosol properties (Friberg et al., 2021; In Prep). The validated LEO-GEO winds and height algorithm provides plume dynamics data with an accuracy of 200 m vertical resolution for plume height and 0.5 m/s for plume speed. Using these stereo algorithms, we can determine if fire plumes stay within or shoot above PBL, which plays a critical role in plume transport and air quality. From the GEO-based observations of dynamic wildfire aerosol loading dispersion, height, and winds, we can track wildfire development at a sub-hourly frequency and capture extreme and/or rare events such as pyroCb that often occur in a short period of time and are largely missed by LEO satellites.</p><p> </p><p><strong>References:</strong></p><p>Carr, J.L., Wu, D.L., Daniels, J., Friberg, M.D., Bresky, W., Madani, H. “GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring,” Remote Sensing, 2020 https://doi.org/10.3390/rs12223779</p><p>Carr, J.L., D.L. Wu, R.E. Wolfe, H. Madani, G. Lin, B. Tan, “Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES,” Remote Sensing, 2019, Satellite Winds Special Issue https://doi.org/10.3390/rs11182100</p><p>Carr, J.L., D.L. Wu, M.A. Kelly, and J. Gong, “MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds,” Remote Sensing, 2018, MISR Special Issue. https://www.mdpi.com/2072-4292/10/12/1885</p><p>Limbacher, J. A., R. A. Kahn, and M. D. Friberg “A Multi-Angle Geostationary Aerosol Retrieval Algorithm,” 2021 [<strong>In Prep</strong>].</p><p>Zou, Y., O’Neill, S.M., Larkin, N.K., Alvarado, E.C., Solomon, R., Mass, C., Liu, Y., Odman, M.T., Shen, H. “Machine learning based integration of high-resolution wildfire smoke simulations and observations for regional health impact assessment. International Journal of Environmental Research and Public Health, 2019. https://doi.org/10.3390/ijerph16122137</p><p>Friberg, M.D., Wu, D.L., Carr, J.L., Limbacher, J. A., Zou<sup>, </sup>Y., O’Neill, S. “Diurnal Observations of Wildfires Boundary Layer Dynamics and Aerosol Plume Convection using Stereo-Imaging Techniques,” 2021 [<strong>In Prep</strong>].</p>

Measuring Stem Diameter of Sorghum Plants in the Field Using a High-Throughput Stereo Vision System

HighlightsA custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping.Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions.Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach.PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes.Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.