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

Learned Enrichment of Top-View Grid Maps Improves Object Detection