Vehicle localization by lidar point correlation improved by change detection

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Schlichting, A.; Brenner, C.: Vehicle localization by lidar point correlation improved by change detection. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 703-710. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B1-703-2016

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/691

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Sum total of downloads: 453




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LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.
License of this version: CC BY 3.0 Unported
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 224 49.45%
2 image of flag of China China 53 11.70%
3 image of flag of United States United States 30 6.62%
4 image of flag of Japan Japan 18 3.97%
5 image of flag of United Kingdom United Kingdom 16 3.53%
6 image of flag of Korea, Republic of Korea, Republic of 15 3.31%
7 image of flag of Taiwan Taiwan 9 1.99%
8 image of flag of Europe Europe 9 1.99%
9 image of flag of Bangladesh Bangladesh 6 1.32%
10 image of flag of Australia Australia 5 1.10%
    other countries 68 15.01%

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