U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based
  Relocalization
Andrea Boscolo Camiletto Alfredo Bochicchio Alexander Liniger Dengxin Dai Abel Gawel
Efficient relocalization is essential for intelligent vehicles when GPS
reception is insufficient or sensor-based localization fails. Recent advances
in Bird's-Eye-View (BEV) segmentation allow for accurate estimation of local
scene appearance and in turn, can benefit the relocalization of the vehicle.
However, one downside of BEV methods is the heavy computation required to
leverage the geometric constraints. This paper presents U-BEV, a U-Net inspired
architecture that extends the current state-of-the-art by allowing the BEV to
reason about the scene on multiple height layers before flattening the BEV
features. We show that this extension boosts the performance of the U-BEV by up
to 4.11 IoU. Additionally, we combine the encoded neural BEV with a
differentiable template matcher to perform relocalization on neural SD-map
data. The model is fully end-to-end trainable and outperforms transformer-based
BEV methods of similar computational complexity by 1.7 to 2.8 mIoU and
BEV-based relocalization by over 26% Recall Accuracy on the nuScenes dataset.