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    "https://arxiv.org/abs/2006.12567",
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  "dcterms:title": [
    "A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence"
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  "dcterms:creator": [
    "Changhao Chen",
    "Bing Wang",
    "Chris Xiaoxuan Lu",
    "Niki Trigoni",
    "Andrew Markham"
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  "dcterms:description": [
    "Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems."
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  "dcterms:subject": [
    "cs.CV (arxiv)",
    "cs.LG (arxiv)",
    "cs.RO (arxiv)",
    "eess.IV (arxiv)"
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  "dcterms:language": [
    "en"
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    "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive"
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  "dcterms:issued": [
    "2020-06-22"
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  "dcterms:modified": [
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