Local map Construction Methods with SD map: A Novel Survey

• URL: http://arxiv.org/abs/2409.02415v1
• Date: Wed, 4 Sep 2024 03:41:42 GMT
• Title: Local map Construction Methods with SD map: A Novel Survey
• Authors: Jiaqi Li, Pingfan Jia, Jiaxing Chen, Jiaxi Liu, Lei He,
• Abstract summary: This paper provides a comprehensive overview of the latest advancements in the integration of SD map as prior information for Local map perception methods. The article addresses pertinent issues and future challenges with the aim of guiding researchers in understanding the current trends and methodologies prevalent in the field.
• Score: 4.493862236612883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, significant academic advancements have been made in the field of autonomous vehicles, with Local maps emerging as a crucial component of autonomous driving technology. Local maps not only provide intricate details of road networks but also serve as fundamental inputs for critical tasks such as vehicle localization, navigation, and decision-making. Given the characteristics of SD map (Standard Definition Map), which include low cost, ease of acquisition, and high versatility, perception methods that integrate SD map as prior information have demonstrated significant potential in the field of Local map perception. The purpose of this paper is to provide researchers with a comprehensive overview and summary of the latest advancements in the integration of SD map as prior information for Local map perception methods. This review begins by introducing the task definition and general pipeline of local map perception methods that incorporate SD maps as prior information, along with relevant public datasets. And then it focuses on the representation and encoding methods of multi-source information, as well as the methods for fusing multi-source information. In response to this burgeoning trend, this article presents a comprehensive and meticulous overview of the diverse research efforts in this particular field. Finally, the article addresses pertinent issues and future challenges with the aim of guiding researchers in understanding the current trends and methodologies prevalent in the field.

Related papers

• A roadmap for generative mapping: unlocking the power of generative AI for map-making [1.128529637069462]
  This paper highlights the key applications of generative AI in map-making. It identifies the specific technologies required and the challenges of using current methods. It provides a roadmap for developing a generative mapping system (GMS) to make map-making more accessible.
  arXiv  Detail & Related papers  (2024-10-21T08:29:43Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Detecting Omissions in Geographic Maps through Computer Vision [18.36056648425432]
  We develop and evaluate a method for automatically identifying maps that depict specific regions and feature landmarks with designated names. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks.
  arXiv  Detail & Related papers  (2024-07-15T13:26:58Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• 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)
• Neural Map Prior for Autonomous Driving [17.198729798817094]
  High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. Traditional method of creating offline HD maps involves labor-intensive manual annotation processes. Recent studies have proposed an alternative approach that generates local maps using online sensor observations. In this study, we propose Neural Map Prior (NMP), a neural representation of global maps.
  arXiv  Detail & Related papers  (2023-04-17T17:58:40Z)
• Guided Depth Map Super-resolution: A Survey [88.54731860957804]
  Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image. A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR.
  arXiv  Detail & Related papers  (2023-02-19T15:43:54Z)
• Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation [87.52136927091712]
  We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
  arXiv  Detail & Related papers  (2022-10-14T04:23:27Z)
• A Survey on Visual Map Localization Using LiDARs and Cameras [0.0]
  We define visual map localization as a two-stage process. At the stage of place recognition, the initial position of the vehicle in the map is determined by comparing the visual sensor output with a set of geo-tagged map regions of interest. At the stage of map metric localization, the vehicle is tracked while it moves across the map by continuously aligning the visual sensors' output with the current area of the map that is being traversed.
  arXiv  Detail & Related papers  (2022-08-05T20:11:18Z)
• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence [48.67755344239951]
  We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping. It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
  arXiv  Detail & Related papers  (2020-06-22T19:01:21Z)

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.