Variable reordering strategies for SLAM

Pratik Agarwal, Edwin Olson
{"title":"Variable reordering strategies for SLAM","authors":"Pratik Agarwal, Edwin Olson","doi":"10.1109/IROS.2012.6385473","DOIUrl":null,"url":null,"abstract":"State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering; but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"3 1","pages":"3844-3850"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2012.6385473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34

Abstract

State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering; but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.
SLAM的可变重排序策略
最先进的状态估计和感知方法利用最小二乘优化方法对噪声传感器数据进行有效的推断。这种效率大部分是通过使用稀疏矩阵分解方法实现的。底层矩阵分解的稀疏结构使得这些优化方法易于处理,这高度依赖于变量重排序的选择;但在SLAM学界还没有对排序方法进行系统的评价。在本文中,我们评估了各种重新排序技术在基准SLAM数据集上的性能,并根据我们的结果提供了明确的建议。我们还将这些最先进的算法与我们简单且易于实现的算法进行比较,这些算法实现了相当的性能。最后,我们提供的经验证据表明,很少的增益仍然相对于最小度排序的变体。
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
自引率
0.00%
发文量
0
×
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