3D feature point extraction from LiDAR data using a neural network

Download statistics - Document (COUNTER):

Feng, Y.; Schlichting, A.; Brenner, C.: 3D feature point extraction from LiDAR data using a neural network. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 563-569. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B1-563-2016

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/690

Selected time period:

year: 
month: 

Sum total of downloads: 501




Thumbnail
Abstract: 
Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 240 47.90%
2 image of flag of United States United States 51 10.18%
3 image of flag of China China 38 7.58%
4 image of flag of Korea, Republic of Korea, Republic of 25 4.99%
5 image of flag of Taiwan Taiwan 16 3.19%
6 image of flag of India India 15 2.99%
7 image of flag of France France 12 2.40%
8 image of flag of Japan Japan 10 2.00%
9 image of flag of United Kingdom United Kingdom 9 1.80%
10 image of flag of Canada Canada 8 1.60%
    other countries 77 15.37%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse