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Learned Enrichment of Top-View Grid Maps Improves Object Detection

Wirges, Sascha; Yang, Ye; Richter, Sven; Hu, Haohao; Stiller, Christoph

We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generalization by regularizing towards structural knowledge in form of a map fused from multiple adjacent range sensor measurements. This training data can be generated in an automatic fashion, thus does not require manual annotations. We present an evidential framework to generate training data, investigate different model architectures and show that predicting enriched inputs as an additional task can improve object detection performance.

Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 02.03.2020
Sprache Englisch
Identifikator KITopen-ID: 1000129315
Umfang 6 S.
Nachgewiesen in arXiv
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