Fast loop closure detection via binary content
Loop closure detection plays an important rolein reducing localization drift in Simultaneous LocalizationAnd Mapping (SLAM). It aims to find repetitive scenes fromhistorical data to reset localization. To tackle the loop closureproblem, existing methods often...
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sg-ntu-dr.10356-1382482020-04-29T08:54:56Z Fast loop closure detection via binary content Wang, Han Li, Juncheng Ran, Maopeng Xie, Lihua School of Electrical and Electronic Engineering 2019 IEEE 15th International Conference on Control and Automation (ICCA) Engineering::Electrical and electronic engineering Loop Closure Binary Content Loop closure detection plays an important rolein reducing localization drift in Simultaneous LocalizationAnd Mapping (SLAM). It aims to find repetitive scenes fromhistorical data to reset localization. To tackle the loop closureproblem, existing methods often leverage on the matching ofvisual features, which achieve good accuracy but require highcomputational resources. However, feature point based methodsignore the patterns of image, i.e., the shape of the objects aswell as the distribution of objects in an image. It is believedthat this information is usually unique for a scene and can beutilized to improve the performance of traditional loop closuredetection methods. In this paper we leverage and compressthe information into a binary image to accelerate an existingfast loop closure detection method via binary content. Theproposed method can greatly reduce the computational costwithout sacrificing recall rate. It consists of three parts: binarycontent construction, fast image retrieval and precise loopclosure detection. No offline training is required. Our methodis compared with the state-of-the-art loop closure detectionmethods and the results show that it outperforms the traditionalmethods at both recall rate and speed. NRF (Natl Research Foundation, S’pore) Published version 2020-04-29T08:54:56Z 2020-04-29T08:54:56Z 2019 Conference Paper Wang, H., Li, J., Ran, M., & Xie, L. (2019). Fast loop closure detection via binary content. Proceedings of the 2019 IEEE 15th International Conference on Control and Automation (ICCA), 1563-1568. doi:10.1109/ICCA.2019.8899937 https://hdl.handle.net/10356/138248 10.1109/ICCA.2019.8899937 1563 1568 en © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICCA.2019.8899937 application/pdf |
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Engineering::Electrical and electronic engineering Loop Closure Binary Content Wang, Han Li, Juncheng Ran, Maopeng Xie, Lihua Fast loop closure detection via binary content |
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Loop closure detection plays an important rolein reducing localization drift in Simultaneous LocalizationAnd Mapping (SLAM). It aims to find repetitive scenes fromhistorical data to reset localization. To tackle the loop closureproblem, existing methods often leverage on the matching ofvisual features, which achieve good accuracy but require highcomputational resources. However, feature point based methodsignore the patterns of image, i.e., the shape of the objects aswell as the distribution of objects in an image. It is believedthat this information is usually unique for a scene and can beutilized to improve the performance of traditional loop closuredetection methods. In this paper we leverage and compressthe information into a binary image to accelerate an existingfast loop closure detection method via binary content. Theproposed method can greatly reduce the computational costwithout sacrificing recall rate. It consists of three parts: binarycontent construction, fast image retrieval and precise loopclosure detection. No offline training is required. Our methodis compared with the state-of-the-art loop closure detectionmethods and the results show that it outperforms the traditionalmethods at both recall rate and speed. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Wang, Han Li, Juncheng Ran, Maopeng Xie, Lihua |
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Conference or Workshop Item |
author |
Wang, Han Li, Juncheng Ran, Maopeng Xie, Lihua |
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Wang, Han |
title |
Fast loop closure detection via binary content |
title_short |
Fast loop closure detection via binary content |
title_full |
Fast loop closure detection via binary content |
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Fast loop closure detection via binary content |
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Fast loop closure detection via binary content |
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fast loop closure detection via binary content |
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2020 |
url |
https://hdl.handle.net/10356/138248 |
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