• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于视觉-激光融合的自主移动机器人线特征同时定位与建图(SLAM)

Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.

机构信息

School of Electrical and Information Engineering, Jinan University, Zhuhai 519070, Guangdong, China.

出版信息

Sensors (Basel). 2012;12(1):429-52. doi: 10.3390/s120100429. Epub 2012 Jan 4.

DOI:10.3390/s120100429
PMID:22368478
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3279222/
Abstract

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

摘要

本文提出了一种应用于动态环境下同时定位与建图（SLAM）的传感器融合策略。所设计的方法包括两个特征：（i）第一个是融合模块，它综合了激光测距仪获得的线段和单目相机提取的线特征。该策略消除了激光数据中任何由于动态物体瞬间停顿而产生的伪线段。（ii）第二个特征是一种改进的多传感器点估计融合 SLAM（MPEF-SLAM），它结合了两种基于扩展卡尔曼滤波器（EKF）的独立 SLAM 算法：单目和激光 SLAM。与独立的 SLAM 相比，融合 SLAM 中的定位误差降低了。此外，还为单目 SLAM 开发了一种新的数据关联技术，基于单应性变换矩阵。这种数据关联方法放宽了多余的计算。实验结果验证了所提出的传感器融合和数据关联方法的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/10286083755b/sensors-12-00429f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/cfb639e91c9c/sensors-12-00429f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5c8017252723/sensors-12-00429f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/1074c60a0d59/sensors-12-00429f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/6c9f79561668/sensors-12-00429f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5cbedb750384/sensors-12-00429f5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/ea9c90919cf2/sensors-12-00429f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5263087d75b6/sensors-12-00429f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/6ca7b2afcb35/sensors-12-00429f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/10286083755b/sensors-12-00429f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/cfb639e91c9c/sensors-12-00429f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5c8017252723/sensors-12-00429f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/1074c60a0d59/sensors-12-00429f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/6c9f79561668/sensors-12-00429f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5cbedb750384/sensors-12-00429f5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/ea9c90919cf2/sensors-12-00429f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/5263087d75b6/sensors-12-00429f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/6ca7b2afcb35/sensors-12-00429f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc14/3279222/10286083755b/sensors-12-00429f9.jpg

相似文献

1
Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots.基于视觉-激光融合的自主移动机器人线特征同时定位与建图(SLAM)
Sensors (Basel). 2012;12(1):429-52. doi: 10.3390/s120100429. Epub 2012 Jan 4.
2
A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system.一种基于滤波器的单目同时定位与建图（SLAM）系统的鲁棒方法。
Sensors (Basel). 2013 Jul 3;13(7):8501-22. doi: 10.3390/s130708501.
3
Human Collaborative Localization and Mapping in Indoor Environments with Non-Continuous Stereo.基于非连续立体视觉的室内环境中的人类协作定位与地图构建
Sensors (Basel). 2016 Feb 24;16(3):275. doi: 10.3390/s16030275.
4
A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments.一种用于全球定位系统（GPS）受限环境下低成本微型飞行器的多感官同步定位与地图构建（SLAM）系统。
Sensors (Basel). 2017 Apr 8;17(4):802. doi: 10.3390/s17040802.
5
Scale Estimation and Correction of the Monocular Simultaneous Localization and Mapping (SLAM) Based on Fusion of 1D Laser Range Finder and Vision Data.基于 1D 激光测距仪和视觉数据融合的单目同时定位与建图 (SLAM) 的尺度估计与校正。
Sensors (Basel). 2018 Jun 15;18(6):1948. doi: 10.3390/s18061948.
6
Monocular SLAM for autonomous robots with enhanced features initialization.用于具有增强特征初始化的自主机器人的单目同步定位与地图构建
Sensors (Basel). 2014 Apr 2;14(4):6317-37. doi: 10.3390/s140406317.
7
SLAM algorithm applied to robotics assistance for navigation in unknown environments.SLAM 算法在机器人辅助未知环境导航中的应用。
J Neuroeng Rehabil. 2010 Feb 17;7:10. doi: 10.1186/1743-0003-7-10.
8
Biomimetic navigation system using a polarization sensor and a binocular camera.基于偏振传感器和双目相机的仿生导航系统。
J Opt Soc Am A Opt Image Sci Vis. 2022 May 1;39(5):847-854. doi: 10.1364/JOSAA.453318.
9
Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles.可训练的四元数扩展卡尔曼滤波器与多头注意力机制在自主地面车辆中的航位推算
Sensors (Basel). 2022 Oct 11;22(20):7701. doi: 10.3390/s22207701.
10
Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot.在基于卡尔曼滤波的移动机器人定位中使用 LRF 传感器。
ISA Trans. 2010 Jan;49(1):145-53. doi: 10.1016/j.isatra.2009.09.009. Epub 2009 Oct 13.

引用本文的文献

1
LiDAR-Stabilised GNSS-IMU Platform Pose Tracking.激光雷达稳定的 GNSS-IMU 平台姿态跟踪。
Sensors (Basel). 2022 Mar 14;22(6):2248. doi: 10.3390/s22062248.
2
Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments.基于传感器融合的动态环境中同时定位与地图构建时消除移动物体的方法
Sensors (Basel). 2021 Jan 1;21(1):230. doi: 10.3390/s21010230.
3
Structured Laser Light Improves Tripping Hazard Recognition for People with Visual Impairments.结构化激光灯改善视障人士对绊倒危险的识别。

本文引用的文献

1
MonoSLAM: real-time single camera SLAM.单目即时定位与地图构建（MonoSLAM）：实时单目相机即时定位与地图构建
IEEE Trans Pattern Anal Mach Intell. 2007 Jun;29(6):1052-67. doi: 10.1109/TPAMI.2007.1049.
Transl Vis Sci Technol. 2020 Aug 4;9(9):6. doi: 10.1167/tvst.9.9.6. eCollection 2020 Aug.
4
Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation.基于激光和视觉的自主导航传感器数据融合技术综述。
Sensors (Basel). 2020 Apr 12;20(8):2180. doi: 10.3390/s20082180.
5
A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping.基于协方差旅行商问题算法的地标同时定位与建图的重访方法。
Sensors (Basel). 2019 Nov 10;19(22):4910. doi: 10.3390/s19224910.
6
RGB-D SLAM Using Point-Plane Constraints for Indoor Environments.用于室内环境的基于点平面约束的RGB-D同步定位与地图构建
Sensors (Basel). 2019 Jun 17;19(12):2721. doi: 10.3390/s19122721.
7
Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.基于扩展线图的 3D LIDAR 精确车辆定位
Sensors (Basel). 2018 Sep 20;18(10):3179. doi: 10.3390/s18103179.
8
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area.基于垂直角特征的城市区域3D激光雷达精确车辆定位
Sensors (Basel). 2016 Aug 10;16(8):1268. doi: 10.3390/s16081268.
9
Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity.基于图结构的同时定位与地图构建：在存在激光扫描模糊性的环境中使用二维激光扫描和单目相机图像的混合方法
Sensors (Basel). 2015 Jul 3;15(7):15830-52. doi: 10.3390/s150715830.
10
Sensor fusion of cameras and a laser for city-scale 3D reconstruction.用于城市规模三维重建的摄像头与激光传感器融合技术
Sensors (Basel). 2014 Nov 4;14(11):20882-909. doi: 10.3390/s141120882.