3D Modeling of the airport environment for fast and accurate LiDAR semantic segmentation of apron operations

Braßel, Hannes; Zouhar, Alexander; Fricke, Hartmut

doi:10.1109/dasc50938.2020.9256495

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Airport authorities use radar to monitor airplane traffic, which provides only coarse positioning and almost no attitude information. Imaging sensors are especially effective to acquire a large volume of data to extract objects, such as aircraft bodies, and then track them based on image sequences [76]. In particular, LiDAR is a suitable remote sensing technology for this type of data acquisition, as it directly captures 3D data and is reasonably fast.…”

Section: ) Monitoring Aircraft Motionsmentioning

confidence: 99%

Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds

et al. 2023

View full text Add to dashboard Cite

Monitoring critical infrastructure is of great importance for resilience against current and emerging hazards. In Canada, at the national level, critical infrastructure is strategically classified into the following 10 sectors: energy and utilities, finance, food, transportation, government, information and communication technology, health, water, safety, and manufacturing. Many of these critical infrastructures may be effectively monitored using emerging technologies such as light detection and ranging (LiDAR). LiDAR technology enables active remote sensing from airborne and terrestrial systems. Accordingly, LiDAR can be used in a wide range of applications related to the monitoring of critical infrastructure to promote resilience. This survey provides a comprehensive, and structured literature overview of the LiDAR technology currently available for commercial and research use, and also provides a detailed review of the relevant applications of LiDAR for critical infrastructure monitoring. Several LiDAR-based applications in areas of critical infrastructure such as terrestrial and air transportation, gas, oil, and water pipelines, energy generation and distribution, and security are discussed. A summary of the LiDAR data sets that are currently available for use is also presented.

show abstract

Section: ) Monitoring Aircraft Motionsmentioning

confidence: 99%

Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Free repositories offer a wide choice of 3D objects for this case (see Section 1.1 ). The need for synthetic data in such applications has been demonstrated e.g., in [ 64 ], where also various sensors are simulated in a virtual environment, although no general framework for the creation of such 3D worlds is provided. Our add-on BLAINDER provides a principled methodology and open-source implementation for such use cases.…”

Section: Modules Of the Implementationmentioning

confidence: 99%

BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Reitmann

Neumann

Jung

2021

Sensors

View full text Add to dashboard Cite

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

show abstract

3D Modeling of the airport environment for fast and accurate LiDAR semantic segmentation of apron operations

Cited by 2 publications

References 15 publications

Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds

Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds

BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Contact Info

Product

Resources

About