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Anomaly Detection in Lidar Data by Combining Supervised and Self-Supervised Methods

Sartoris, Finn 1
1 Karlsruher Institut für Technologie (KIT)


To enable safe autonomous driving, a reliable and redundant perception of the environment is
required. In the context of autonomous vehicles, the perception is mainly based on machine learning
models that analyze data from various sensors such as camera, Radio Detection and Ranging
(radar), and Light Detection and Ranging (lidar). Since the performance of the models depends
significantly on the training data used, it is necessary to ensure perception even in situations that
are difficult to analyze and deviate from the training dataset. These situations are called corner
cases or anomalies.
Motivated by the need to detect such situations, this thesis presents a new approach for detecting
anomalies in lidar data by combining Supervised (SV) and Self-Supervised (SSV) models. In particular,
inconsistent point-wise predictions between a SV and a SSV part serve as an indication
of anomalies arising from the models used themselves, e.g., due to lack of knowledge. The SV
part is composed of a SV semantic segmentation model and a SV moving object segmentation
model, which together assign a semantic motion class to each point of the point cloud. ... mehr

Volltext §
DOI: 10.5445/IR/1000147668
Veröffentlicht am 07.06.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Publikationsdatum 01.06.2022
Sprache Englisch
Identifikator KITopen-ID: 1000147668
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XI, 61 S.
Art der Arbeit Abschlussarbeit - Bachelor
Referent/Betreuer Zöllner, J. M.
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