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Sponsorship: BBSRC RONO: BBS/E/W/10961A0

Plant phenotyping involves the measurement, ideally objectively, of characteristics or traits. Traditionally, this is either limited to tedious and sparse manual measurements, often acquired destructively, or coarse image-based 2D measurements. 3D sensing technologies (3D laser scanning, structured light and digital photography) are increasingly incorporated into mass produced consumer goods and have the potential to automate the process, providing a cost-effective alternative to current commercial phenotyping platforms. We evaluate the performance, cost and practicability for plant phenotyping and present a 3D reconstruction method from multi-view images acquired with a domestic quality camera. This method consists of the following steps: (i) image acquisition using a digital camera and turntable; (ii) extraction of local invariant features and matching from overlapping image pairs; (iii) estimation of camera parameters and pose based on Structure from Motion(SFM); and (iv) employment of a patch based multi-view stereo technique to implement a dense 3D point cloud. We conclude that the proposed 3D reconstruction is a promising generalized technique for the non-destructive phenotyping of various plants during their whole growth cycles

Measuring geometric features in plant specimens either quantitatively or qualitatively, is crucial for plant phenotyping. However, traditional measurement methods tend to be manual and can be tedious, or employ coarse 2D imaging techniques. Emerging 3D imaging technologies show much promise in capturing architectural complexity. However, automated 3D acquisition and accurate estimation of plant morphology for the construction of quantitative plant models remain largely aspiration. In this paper, we propose an approach for segmentation and angle estimation directly from dense 3D plant point clouds. Experimental results show that the approach is efficient and reliable, and appears to be a promising 3D acquisition and measurement solution to plant phenotyping for structural analysis and for building Functional-Structural Plant Models (FSPM)

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Monocular camera and Lidar are the two most commonly used sensors in unmanned vehicles. Combining the advantages of the two is the current research focus of SLAM and semantic analysis. In this paper, we propose an improved SLAM and semantic reconstruction method based on the fusion of Lidar and monocular vision. We fuse the semantic image with the low-resolution 3D Lidar point clouds and generate dense semantic depth maps. Through visual odometry, ORB feature points with depth information are selected to improve positioning accuracy. Our method uses parallel threads to aggregate 3D semantic point clouds while positioning the unmanned vehicle. Experiments are conducted on the public CityScapes and KITTI Visual Odometry datasets, and the results show that compared with the ORB-SLAM2 and DynaSLAM, our positioning error is approximately reduced by 87%; compared with the DEMO and DVL-SLAM, our positioning accuracy improves in most sequences. Our 3D reconstruction quality is better than DynSLAM and contains semantic information. The proposed method has engineering application value in the unmanned vehicles field.