Towards 3D Point cloud based object maps for household environments

Rusu, Radu Bogdan; Márton, Zoltán-Csaba; Blodow, Nico; Dolha, Mihai Emanuel; Beetz, Michael

doi:10.1016/j.robot.2008.08.005

Cited by 884 publications

(481 citation statements)

References 22 publications

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“…open-source libraries and algorithms, including OpenCV (http://opencv.org; accessed February 2016) and PCL (Fischler and Bolles, 1981;Besl and McKay, 1992;Rabbani et al, 2006;Rusu et al, 2008;Rusu and Cousins, 2011;Buch et al, 2013). This point cloud was inspected manually, acquisition and/or registration errors were corrected manually using MeshLab (Cignoni et al, 2008), and the cleaned point cloud was meshed to generate a set of polygons representing the surface of the plant using available open-source software (Bernardini et al, 1999;Corsini et al, 2012;Kazhdan and Hoppe, 2013).…”

Section: Plants Greenhouse Conditions Manual Measurements and Imagmentioning

confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

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Section: Plants Greenhouse Conditions Manual Measurements and Imagmentioning

confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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“…With the transformation, the UAV-Point cloud got scaled by the factor 2.43. To eliminate outliers and single points we filtered the point cloud with a neighbour relation approach described in [Rusu et al, 2008]. Figure 5 shows a view into the cleaned point cloud.…”

Section: Discussionmentioning

confidence: 99%

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Fritz

Kattenborn

Koch

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

138

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ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

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“…7 (a) is the yaw 3D point cloud data obtained directly from the sensor. Then we use the stochastic filter [25], voxel grid filter [26], and random sample consensus (RANSAC) [27] pre-processing algorithms to obtain a clean 3D model and the result is given in Fig. 7 (b).…”

Section: A Experiments Imentioning

confidence: 99%

An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

Mei

2015

Neurocomputing

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Towards 3D Point cloud based object maps for household environments

Cited by 884 publications

References 22 publications

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

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