Trainable Hypervectors Encoding for Efficient 3D Loop-Closure Detection on Edge Devices

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Jeng-Lun Shieh;Shanq-Jang Ruan
{"title":"Trainable Hypervectors Encoding for Efficient 3D Loop-Closure Detection on Edge Devices","authors":"Jeng-Lun Shieh;Shanq-Jang Ruan","doi":"10.1109/LRA.2024.3505820","DOIUrl":null,"url":null,"abstract":"Loop-closure detection plays a critical role in simultaneous localization and mapping (SLAM) systems. The primary task of loop-closure detection involves analyzing previously visited locations and correcting mapping errors, which typically stem from intrinsic noise in sensor data and accumulate over time. However, the burden of storing and querying/searching for previously visited information continues to increase with time. Consequently, reducing the amount of data stored becomes increasingly important. In this study, we propose a trainable hypervectors (THV) encoder, integrating quantization and a lookup table (LUT) to significantly enhance execution speed. Additionally, we employ a triangular mask in second-order pooling (SOP) for filtering extraneous features in the encoder and introduce binary quadruplet loss to efficiently train binary feature representations. We evaluate our method extensively on the KITTI, MulRan and Wild-Places datasets. The experiments demonstrate that our method substantially improves efficiency while maintaining accuracy. Moreover, our method effectively utilizes the 3D-NAND flash in-memory computing technique to improve execution performance.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"168-175"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10766645/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Loop-closure detection plays a critical role in simultaneous localization and mapping (SLAM) systems. The primary task of loop-closure detection involves analyzing previously visited locations and correcting mapping errors, which typically stem from intrinsic noise in sensor data and accumulate over time. However, the burden of storing and querying/searching for previously visited information continues to increase with time. Consequently, reducing the amount of data stored becomes increasingly important. In this study, we propose a trainable hypervectors (THV) encoder, integrating quantization and a lookup table (LUT) to significantly enhance execution speed. Additionally, we employ a triangular mask in second-order pooling (SOP) for filtering extraneous features in the encoder and introduce binary quadruplet loss to efficiently train binary feature representations. We evaluate our method extensively on the KITTI, MulRan and Wild-Places datasets. The experiments demonstrate that our method substantially improves efficiency while maintaining accuracy. Moreover, our method effectively utilizes the 3D-NAND flash in-memory computing technique to improve execution performance.
边缘设备上三维闭环检测的可训练超向量编码
闭环检测在同步定位与制图系统中起着至关重要的作用。闭环检测的主要任务包括分析以前访问过的位置并纠正地图错误,这些错误通常源于传感器数据中的固有噪声,并随着时间的推移而积累。然而,存储和查询/搜索以前访问过的信息的负担随着时间的推移而不断增加。因此,减少存储的数据量变得越来越重要。在这项研究中,我们提出了一个可训练的超向量(THV)编码器,整合量化和查找表(LUT),以显着提高执行速度。此外,我们在二阶池化(SOP)中使用三角形掩模来过滤编码器中的无关特征,并引入二进制四重损失来有效地训练二进制特征表示。我们在KITTI、MulRan和Wild-Places数据集上广泛地评估了我们的方法。实验表明,该方法在保持精度的同时,大大提高了效率。此外,我们的方法有效地利用了3D-NAND闪存内存计算技术来提高执行性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信