Conference Papers Year : 2023

Mind the map! Accounting for existing maps when estimating online HDMaps from sensors.

Abstract

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. To prove this, we identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We then 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.
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Dates and versions

hal-04385135 , version 1 (10-01-2024)
hal-04385135 , version 2 (29-01-2025)

Identifiers

Cite

Rémy Sun, Li Yang, Diane Lingrand, Frédéric Precioso. Mind the map! Accounting for existing maps when estimating online HDMaps from sensors.. Winter conference on Applications of Computer Vision - WACV 2025, Feb 2025, Tucson (USA), United States. ⟨10.48550/arXiv.2311.10517⟩. ⟨hal-04385135v2⟩
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