Mind the map! Accounting for existing map information when estimating online HDMaps from sensor

• URL: http://arxiv.org/abs/2311.10517v2
• Date: Thu, 14 Mar 2024 15:09:08 GMT
• Title: Mind the map! Accounting for existing map information when estimating online HDMaps from sensor
• Authors: Rémy Sun, Li Yang, Diane Lingrand, Frédéric Precioso,
• Abstract summary: Estimating HDMaps from sensors promises to significantly lighten costs. We propose to account for existing maps of the precise situation studied when estimating HDMaps. We introduce MapEX, a novel online HDMap estimation framework.
• Score: 15.275704436439012
• License: http://creativecommons.org/licenses/by/4.0/
• 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. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated).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.

Related papers

• P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors [33.73570824028748]
  We present P-MapNet, in which the letter P highlights the fact that we focus on incorporating map priors to improve model performance. Our attention-based architecture adaptively attends to relevant SDMap skeletons and significantly improves performance. P-MapNet is a far-seeing solution that brings larger improvements on longer ranges.
  arXiv  Detail & Related papers  (2024-03-15T17:59:53Z)
• CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence [9.652629004863364]
  We develop a large-scale benchmark dataset for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. These well-labelled datasets would facilitate the state-of-the-art machine intelligence technologies to conduct map feature detection, map pattern recognition and map content retrieval.
  arXiv  Detail & Related papers  (2023-12-14T01:54:38Z)
• Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps [51.24861159115138]
  Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative. We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Representations from transFormers. This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods.
  arXiv  Detail & Related papers  (2023-11-07T15:42:22Z)
• Online Map Vectorization for Autonomous Driving: A Rasterization Perspective [58.71769343511168]
  We introduce a newization-based evaluation metric, which has superior sensitivity and is better suited to real-world autonomous driving scenarios. We also propose MapVR (Map Vectorization via Rasterization), a novel framework that applies differentiableization to preciseized outputs and then performs geometry-aware supervision on HD maps.
  arXiv  Detail & Related papers  (2023-06-18T08:51:14Z)
• VectorMapNet: End-to-end Vectorized HD Map Learning [18.451587680552464]
  We introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps friendly to downstream autonomous driving tasks. Experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argo2 dataset.
  arXiv  Detail & Related papers  (2022-06-17T17:57:13Z)
• MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale [1.5894241142512051]
  We present MapReader, a free, open-source software library written in Python for analyzing large map collections (scanned or born-digital) MapReader allows users with little or no computer vision expertise to retrieve maps via web-servers. We show how the outputs from the MapReader pipeline can be linked to other, external datasets.
  arXiv  Detail & Related papers  (2021-11-30T17:37:01Z)
• HDMapNet: An Online HD Map Construction and Evaluation Framework [23.19001503634617]
  HD map construction is a crucial problem for autonomous driving. Traditional HD maps are coupled with centimeter-level accurate localization which is unreliable in many scenarios. Online map learning is a more scalable way to provide semantic and geometry priors to self-driving vehicles.
  arXiv  Detail & Related papers  (2021-07-13T18:06:46Z)
• HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [81.86923212296863]
  HD maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. There are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps.
  arXiv  Detail & Related papers  (2021-06-28T17:59:30Z)
• MP3: A Unified Model to Map, Perceive, Predict and Plan [84.07678019017644]
  MP3 is an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations.
  arXiv  Detail & Related papers  (2021-01-18T00:09:30Z)
• HDNET: Exploiting HD Maps for 3D Object Detection [99.49035895393934]
  We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. We design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
  arXiv  Detail & Related papers  (2020-12-21T21:59:54Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.