Graph-connected components for filtering urban LiDAR data

Belkhouche, Yassine; Duraisamy, Prakash; Buckles, Bill P.

doi:10.1117/1.jrs.9.096075

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The accuracy of DEM model is the core of this algorithm. There are mainly three ways to obtain DEM data: (1) Measuring on the ground by a hand-held GPS measuring instrument or total station; (2) Obtaining remote sensing images through photoelectric equipment and remote sensing technology, while carrying out three-dimensional reconstruction; (3) Using high-precision equipment such as radar and laser scanner to obtain elevation information like the 3D point cloud [65][66][67][68][69][70][71]. The accuracy of DEM model is the core of this algorithm.…”

Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

“…Wang [50] analyzed the imaging link of the aerial camera for photogrammetry of the target and established a multifactor positioning error analysis model. Furthermore, scholars have established corresponding mathematical models or correction algorithms for different error factors [63][64][65][66][67][68][69][70][71][72][73]. Taking the height error as an example, Qiao [63,64] has established the target positioning algorithm based on the digital elevation model, which solved the problem of excessive target height error caused by the positioning algorithm based on the earth ellipsoid model; HAN [72] used the feature points in the image to obtain height information and locate the target without terrain data, but this method needs to control the imaging distance and the identifiable feature points must be ensured to exist in the graph.…”

Section: Introductionmentioning

confidence: 99%

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Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras’ self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.

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Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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“…Similar algorithms were more recently presented for point clouds (Belkhouche et al, 2015) and DSMs (Beumier and Idrissa, 2015). The filter required a slope S parameter, an area A parameter and a relative gradient R parameter to be provided by the user.…”

Section: Segmentation Filtermentioning

confidence: 99%

“…In comparison, algorithms that label segments/clusters such as (Belkhouche et al, 2015) and (Tóvári and Pfeifer, 2005), divide the point cloud into homogeneous groups of points and then use neighbourhoods, fitted surfaces or properties of the segments to discriminate ground segments from nonground segments.…”

Section: Introductionmentioning

confidence: 99%

Digital Terrain From a Two-Step Segmentation and Outlier-Based Algorithm

Hingee¹,

Caccetta²,

Caccetta³

et al. 2016

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

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ABSTRACT:We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600km 2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho's Match-T algorithm.

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“…• Object candidates have to be segmented in order to classify the environment. This is usually done by connected component analysis or specific object detection [13]. • Shape classification is the next stage, which is investigated in details in the paper.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

Rózsa

Szirányi

2018

IEEE Trans. Intell. Transport. Syst.

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Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.

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Graph-connected components for filtering urban LiDAR data

Cited by 6 publications

References 12 publications

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Digital Terrain From a Two-Step Segmentation and Outlier-Based Algorithm

Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

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