In this work, we research and evaluate end-to-end learning of monocular
semantic-metric occupancy grid mapping from weak binocular ground truth. The
network learns to predict four classes, as well as a camera to bird's eye view
mapping. At the core, it utilizes a variational encoder-decoder network that
encodes the front-view visual information of the driving scene and subsequently
decodes it into a 2-D top-view Cartesian coordinate system. The evaluations on
Cityscapes show that the end-to-end learning of semantic-metric occupancy grids
outperforms the deterministic mapping approach with flat-plane assumption by
more than 12% mean IoU. Furthermore, we show that the variational sampling with
a relatively small embedding vector brings robustness against vehicle dynamic
perturbations, and generalizability for unseen KITTI data. Our network achieves
real-time inference rates of approx. 35 Hz for an input image with a resolution
of 256x512 pixels and an output map with 64x64 occupancy grid cells using a
Titan V GPU.

该研究使用单目摄像头的弱立体视觉信息，通过变分编码器 - 解码器网络实现了从前视图到鸟瞰图的语义度量占用栅格地图学习，并优于传统方法，在城市街景数据集上 mean IoU 提升 12% 以上，并实现了对未见数据的鲁棒性和实时推理速度。