While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps, with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper, we propose to account for existing maps of the precise situation studied when estimating HDMaps. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.

翻译：高清地图（HDMap）是自动驾驶的关键组成部分，但其获取和维护成本高昂。因此，从传感器估计这些地图有望大幅降低成本。然而，现有的估计方法忽略了已有高清地图——目前的方法最多仅能对低质量地图进行地理定位，或考虑已知地图的通用数据库。本文提出在估计高清地图时，应考虑所研究精确场景中的现有地图。我们识别出三类合理的可用现有地图（极简地图、含噪地图和过时地图）。同时引入MapEX——一种新颖的在线高清地图估计框架，该框架通过将地图元素编码为查询令牌并优化经典基于查询的地图估计模型训练时使用的匹配算法，来实现对现有地图的利用。我们在nuScenes数据集上证明MapEX带来了显著改进。例如，在给定含噪地图的情况下，MapEX相较于其基础模型MapTRv2检测器提升了38%，相较于当前最先进方法提升了8%。