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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
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Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications

Authors: Schlager, Birgit; Goelles, Thomas; Muckenhuber, Stefan; Watzenig, Daniel;

Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications

Abstract

Data description of contamination measurements with lidar sensors RIEGL LD05-A20 and Ouster OS1-64 Photos of the measurement setup We provide photos of the measurement setup and the contaminations applied in the folder "/photos". Riegl LD05-A20 data The data of the Riegl LD05-A20 can be found in the folder "riegl_LD05-A20_full_waveforms" - one file per experiment. An example notebook for reading the data is provided in "/notebooks/example_riegl_LD05-A20.ipynb". Note that the files contain only the prominent peaks of the full waveform calculated by the V08Wave software provided by RIEGL. If you are interested in the entire full waveforms, please contact the authors. Ouster OS1-64 The data of the Ouster OS1-64 can be found in the folder "ouster_OS1-64_point_clouds" - one folder per experiment. An example notebook for reading the data is provided in "/notebooks/example_ouster_OS1-64.ipynb". The python package pointcloudset (https://github.com/virtual-vehicle/pointcloudset) and its documentation is suggested for further data analytics of the point cloud data.

This work was supported in part by the University of Graz; in part by the COMET K2 Competence Centers for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK); in part by the Austrian Federal Ministry for Digital and Economic Affairs (BMDW); in part by the Province of Styria (Dept. 12); and in part by the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion Agency (FFG) has been authorized for the Programme Management.

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Keywords

fault diagnosis, autonomous vehicles, measurement errors

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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