Deep Learning for Visual Localization and Mapping: A Survey

• URL: http://arxiv.org/abs/2308.14039v1
• Date: Sun, 27 Aug 2023 08:25:00 GMT
• Title: Deep Learning for Visual Localization and Mapping: A Survey
• Authors: Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham
• Abstract summary: We provide a comprehensive survey, and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising to localization and mapping; and how deep learning should be applied to solve this problem.
• Score: 45.20992113099714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potentials to track self-motion and estimate environmental model accurately and robustly for mobile agents. In this work, we provide a comprehensive survey, and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising to localization and mapping; how deep learning should be applied to solve this problem. To this end, a series of localization and mapping topics are investigated, from the learning based visual odometry, global relocalization, to mapping, and simultaneous localization and mapping (SLAM). It is our hope that this survey organically weaves together the recent works in this vein from robotics, computer vision and machine learning communities, and serves as a guideline for future researchers to apply deep learning to tackle the problem of visual localization and mapping.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Cross-view geo-localization: a survey [1.3686993145787065]
  Cross-view geo-localization has garnered notable attention in the realm of computer vision, spurred by the widespread availability of copious geotagged datasets. This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain.
  arXiv  Detail & Related papers  (2024-06-14T05:14:54Z)
• Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review [2.1383489372142503]
  Millions of people live in slums or informal settlements with poor living conditions in many major cities around the world. Remote sensing based mapping of slums has emerged as a prominent research area. Deep Learning has added a new dimension to this field as it allows automated analysis of satellite imagery to identify complex spatial patterns associated with slums.
  arXiv  Detail & Related papers  (2024-06-12T09:31:52Z)
• Domain Generalization through Meta-Learning: A Survey [6.524870790082051]
  Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data. This survey paper delves into the realm of meta-learning with a focus on its contribution to domain generalization.
  arXiv  Detail & Related papers  (2024-04-03T14:55:17Z)
• Explaining Deep Models through Forgettable Learning Dynamics [12.653673008542155]
  We visualize the learning behaviour during training by tracking how often samples are learned and forgotten in subsequent training epochs. Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model.
  arXiv  Detail & Related papers  (2023-01-10T21:59:20Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• Weakly Supervised Object Localization and Detection: A Survey [145.5041117184952]
  weakly supervised object localization and detection plays an important role for developing new generation computer vision systems. We review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field
  arXiv  Detail & Related papers  (2021-04-16T06:44:50Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• 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.