  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.
