长走廊环境下视觉特征辅助的移动机器人定位研究

葛耿育; 张毅; 王维; 胡立鹤; 王洋; 蒋勤

doi:10.1631/FITEE.2200208

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长走廊环境下视觉特征辅助的移动机器人定位研究

常规文章 | Updated：2023-07-03

- 长走廊环境下视觉特征辅助的移动机器人定位研究
  Enhanced Publication
- Visual-feature-assisted mobile robot localization in a long corridor environment
- 信息与电子工程前沿(英文版) 2023年24卷第6期页码：876-889
- Affiliations：
  
  1.School of Computer Science and Technology, Chongqing University of Posts and Telecommunications,Chongqing 400065, China
  2.School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications,Chongqing 400065, China
  3.School of Information Engineering, Zunyi Normal University,Zunyi 563006, China
- Author bio：
  
  †E-mail: gegengyu_2021@163.com
  ‡ Corresponding author
- Funds：
  
  the National Natural Science Foundation of China(61703067;61803058;51604056;51775076);the Science and Technology Research Project of Chongqing Education Commission, China(KJ1704072);the Doctoral Talent Train Project of Chongqing University of Posts and Telecommunications, China(BYJS202006)
- DOI：10.1631/FITEE.2200208
  中图分类号： TP242.6
- 纸质出版日期：2023-06-0 ，
  
  收稿日期：2022-05-14，
  
  修回日期：2022-11-15，
- Accepted：
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葛耿育, 张毅, 王维, 等. 长走廊环境下视觉特征辅助的移动机器人定位研究[J]. 信息与电子工程前沿(英文版), 2023,24(6):876-889.

GENGYU GE, YI ZHANG, WEI WANG, et al. Visual-feature-assisted mobile robot localization in a long corridor environment. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 876-889.
葛耿育, 张毅, 王维, 等. 长走廊环境下视觉特征辅助的移动机器人定位研究[J]. 信息与电子工程前沿(英文版), 2023,24(6):876-889. DOI： 10.1631/FITEE.2200208.

GENGYU GE, YI ZHANG, WEI WANG, et al. Visual-feature-assisted mobile robot localization in a long corridor environment. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 876-889. DOI： 10.1631/FITEE.2200208.

摘要

定位在移动机器人导航系统中起着至关重要的作用，是自主移动的基本能力。在室内环境中，当前主流的定位方案使用2D激光雷达，利用即时定位和建图（SLAM）技术来构建占据栅格地图；然后，基于已知的地图来定位。然而，此类方案仅在具有显著几何特征的区域有效。对于重复、对称或类似结构的区域，例如长走廊，常规粒子过滤方法将失效。为解决这一问题，本文提出一种从粗到细的模式，该模式使用视觉特征辅助长走廊中的移动机器人定位。首先，移动机器人被远程控制，沿着中线从起始位置移动到终点。在移动过程中，使用基于激光的SLAM方法建图。同时，根据关键帧选择策略创建关键帧图像组成的视觉地图。关键帧通过时间戳与机器人的姿势相关联。其次，基于提取的激光扫描距离特征，提出一种移动策略，确定初始粗略位置。这对于移动机器人来说至关重要，因为它给出了机器人需要移动到哪里才能调整姿势的指令。然后，移动机器人根据移动策略以适当的视角捕捉图像，并将其与图像地图进行匹配，以获得粗略的定位。最后，提出一种改进的粒子滤波方法来实现精细定位。实验结果表明，该方法对全局定位是有效和鲁棒的。定位成功率达98.8%，平均移动距离仅0.31米。此外，当移动机器人被绑架到走廊中的另一个位置时，该方法依然有效。

Abstract

Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement. In an indoor environment

the current mainstream localization scheme uses two-dimensional (2D) laser light detection and ranging (LiDAR) to build an occupancy grid map with simultaneous localization and mapping (SLAM) technology; it then locates the robot based on the known grid map. However

such solutions work effectively only in those areas with salient geometrical features. For areas with repeated

symmetrical

or similar structures

such as a long corridor

the conventional particle filtering method will fail. To solve this crucial problem

this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor. First

the mobile robot is remote-controlled to move from the starting position to the end along a middle line. In the moving process

a grid map is built using the laser-based SLAM method. At the same time

a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy. The keyframes are associated with the robot’s poses through timestamps. Second

a moving strategy is proposed

based on the extracted range features of the laser scans

to decide on an initial rough position. This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose. Third

the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization. Finally

an improved particle filtering method is presented to achieve fine localization. Experimental results show that our method is effective and robust for global localization. The localization success rate reaches 98.8% while the average moving distance is only 0.31 m. In addition

the method works well when the mobile robot is kidnapped to another position in the corridor.

关键词

移动机器人定位即时定位和建图（SLAM）走廊环境粒子滤波视觉特征

Keywords

Mobile robotLocalizationSimultaneous localization and mapping (SLAM)Corridor environmentParticle filterVisual features

references

Chen RJ, Yin H, Jiao YM, et al., 2021. Deep samplable observation model for global localization and kidnapping. IEEE Robot Autom Lett, 6(2):2296-2303. https://doi.org/10.1109/LRA.2021.3061339https://doi.org/10.1109/LRA.2021.3061339

Chen XYL, Läbe T, Nardi L, et al., 2020. Learning an overlap-based observation model for 3D LiDAR localization. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4602-4608. https://doi.org/10.1109/IROS45743.2020.9340769https://doi.org/10.1109/IROS45743.2020.9340769

Djosic S, Stojanovic I, Jovanovic M, et al., 2021. Fingerprinting-assisted UWB-based localization technique for complex indoor environments. Exp Syst Appl, 167:114188. https://doi.org/10.1016/j.eswa.2020.114188https://doi.org/10.1016/j.eswa.2020.114188

Fox D, Burgard D, Thrun S, 1999a. Markov localization for mobile robots in dynamic environments. J Artif Intell Res, 11:391-427. https://doi.org/10.1613/jair.616https://doi.org/10.1613/jair.616

Fox D, Burgard W, Dellaert F, et al., 1999b. Monte Carlo localization: efficient position estimation for mobile robots. Proc 16th National Conf on Artificial Intelligence and 11th Conf on Innovative Applications of Artificial Intelligence, p.343-349.

Ge GY, Zhang Y, Wang W, et al., 2022. Text-MCL: autonomous mobile robot localization in similar environment using text-level semantic information. Machines, 10(3):169. https://doi.org/10.3390/machines10030169https://doi.org/10.3390/machines10030169

Grisetti G, Stachniss C, Burgard W, 2007. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans Robot, 23(1):34-46. https://doi.org/10.1109/TRO.2006.889486https://doi.org/10.1109/TRO.2006.889486

Hess W, Kohler D, Rapp H, et al., 2016. Real-time loop closure in 2D LIDAR SLAM. Proc IEEE Int Conf on Robotics and Automation, p.1271-1278. https://doi.org/10.1109/ICRA.2016.7487258https://doi.org/10.1109/ICRA.2016.7487258

Ito S, Endres F, Kuderer M, et al., 2014. W-RGB-D: floor-plan-based indoor global localization using a depth camera and WiFi. Proc IEEE Int Conf on Robotics and Automation, p.417-422. https://doi.org/10.1109/ICRA.2014.6906890https://doi.org/10.1109/ICRA.2014.6906890

Katsikis VN, Mourtas SD, Stanimirović PS, et al., 2022. Solving complex-valued time-varying linear matrix equations via QR decomposition with applications to robotic motion tracking and on angle-of-arrival localization. IEEE Trans Neur Netw Learn Syst, 33(8):3415-3424. https://doi.org/10.1109/TNNLS.2021.3052896https://doi.org/10.1109/TNNLS.2021.3052896

Kim J, Chung W, 2016. Localization of a mobile robot using a laser range finder in a glass-walled environment. IEEE Trans Ind Electron, 63(6):3616-3627. https://doi.org/10.1109/TIE.2016.2523460https://doi.org/10.1109/TIE.2016.2523460

Liu X, Zhou BD, Huang PP, et al., 2021. Kalman filter-based data fusion of Wi-Fi RTT and PDR for indoor localization. IEEE Sens J, 21(6):8479-8490. https://doi.org/10.1109/JSEN.2021.3050456https://doi.org/10.1109/JSEN.2021.3050456

Long SB, He X, Yao C, 2021. Scene text detection and recognition: the deep learning era. Int J Comput Vis, 129(1):‍161-184. https://doi.org/10.1007/s11263-020-01369-0https://doi.org/10.1007/s11263-020-01369-0

Meng J, Wang ST, Xie YL, et al., 2021. Efficient re-localization of mobile robot using strategy of finding a missing person. Measurement, 176:109212. https://doi.org/10.1016/j.measurement.2021.109212https://doi.org/10.1016/j.measurement.2021.109212

Motroni A, Buffi A, Nepa P, 2021. A survey on indoor vehicle localization through RFID technology. IEEE Access, 9:17921-17942. https://doi.org/10.1109/ACCESS.2021.3052316https://doi.org/10.1109/ACCESS.2021.3052316

Muhammad A, Ali MAH, Turaev S, et al., 2022. Novel algorithm for mobile robot path planning in constrained environment. Comput Mater Contin, 71(2):2697-2719. https://doi.org/10.32604/cmc.2022.020873https://doi.org/10.32604/cmc.2022.020873

Mur-Artal R, Montiel JMM, Tardós JD, 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot, 31(5):1147-1163. https://doi.org/10.1109/TRO.2015.2463671https://doi.org/10.1109/TRO.2015.2463671

Naseer T, Burgard W, Stachniss C, 2018. Robust visual localization across seasons. IEEE Trans Robot, 34(2):289-302. https://doi.org/10.1109/TRO.2017.2788045https://doi.org/10.1109/TRO.2017.2788045

Qian C, Zhang HJ, Tang J, et al., 2019. An orthogonal weighted occupancy likelihood map with IMU-aided laser scan matching for 2D indoor mapping. Sensors, 19(7):1742. https://doi.org/10.3390/s19071742https://doi.org/10.3390/s19071742

Rublee E, Rabaud V, Konolige K, et al., 2011. ORB: an efficient alternative to SIFT or SURF. Proc IEEE Conf on Computer Vision, p.2564-2571. https://doi.org/10.1109/ICCV.2011.6126544https://doi.org/10.1109/ICCV.2011.6126544

Thrun S, Fox D, Burgard W, et al., 2001. Robust Monte Carlo localization for mobile robots. Artif Intell, 128(1-2):99-141. https://doi.org/10.1016/S0004-3702(01)00069-8https://doi.org/10.1016/S0004-3702(01)00069-8

Thrun S, Burgard W, Fox D, 2005. Probabilistic Robotics. MIT Press, Cambridge, USA.

Ullah I, Qian SY, Deng ZX, et al., 2021. Extended Kalman filter-based localization algorithm by edge computing in wireless sensor networks. Dig Commun Netw, 7(2):‍187-195. https://doi.org/10.1016/j.dcan.2020.08.002https://doi.org/10.1016/j.dcan.2020.08.002

Valente M, Joly C, de La Fortelle A, 2019. Evidential SLAM fusing 2D laser scanner and stereo camera. Unmanned Syst, 7(3):149-159. https://doi.org/10.1142/S2301385019410012https://doi.org/10.1142/S2301385019410012

Wang FS, Zhang JX, Lin BW, et al., 2018. Two stage particle filter for nonlinear Bayesian estimation. IEEE Access, 6:13803-13809. https://doi.org/10.1109/ACCESS.2018.2808922https://doi.org/10.1109/ACCESS.2018.2808922

Wang XV, Wang LH, 2021. A literature survey of the robotic technologies during the COVID-19 pandemic. J Manuf Syst, 60:823-836. https://doi.org/10.1016/j.jmsy.2021.02.005https://doi.org/10.1016/j.jmsy.2021.02.005

Wu N, Feng LH, Yang AY, 2017. Localization accuracy improvement of a visible light positioning system based on the linear illumination of LED sources. IEEE Photon J, 9(5):7905611. https://doi.org/10.1109/JPHOT.2017.2727643https://doi.org/10.1109/JPHOT.2017.2727643

Xu LC, Feng C, Kamat VR, et al., 2019. An occupancy grid mapping enhanced visual SLAM for real-time locating applications in indoor GPS-denied environments. Autom Constr, 104:230-245. https://doi.org/10.1016/j.autcon.2019.04.011https://doi.org/10.1016/j.autcon.2019.04.011

Yousuf S, Kadri MB, 2021. Information fusion of GPS, INS and odometer sensors for improving localization accuracy of mobile robots in indoor and outdoor applications. Robotica, 39(2):250-276. https://doi.org/10.1017/S0263574720000351https://doi.org/10.1017/S0263574720000351

Zhang L, Chen ZH, Cui W, et al., 2020. WiFi-based indoor robot positioning using deep fuzzy forests. IEEE Int Things J, 7(11):10773-10781. https://doi.org/10.1109/JIOT.2020.2986685https://doi.org/10.1109/JIOT.2020.2986685

Zhao JH, Zhao L, Huang SD, et al., 2020. 2D laser SLAM with general features represented by implicit functions. IEEE Robot Autom Lett, 5(3):4329-4336. https://doi.org/10.1109/LRA.2020.2996795https://doi.org/10.1109/LRA.2020.2996795

Zhao JH, Li TC, Yang T, et al., 2021. 2D laser SLAM with closed shape features: Fourier series parameterization and submap joining. IEEE Robot Autom Lett, 6(2):1527-1534. https://doi.org/10.1109/LRA.2021.3058065https://doi.org/10.1109/LRA.2021.3058065

Zhao ZQ, Zheng P, Xu ST, et al., 2019. Object detection with deep learning: a review. IEEE Trans Neur Netw Learn Syst, 30(11):3212-3232. https://doi.org/10.1109/TNNLS.2018.2876865https://doi.org/10.1109/TNNLS.2018.2876865

Zimmerman N, Wiesmann L, Guadagnino T, et al., 2022. Robust onboard localization in changing environments exploiting text spotting. https://doi.org/10.48550/arXiv.2203.12647https://doi.org/10.48550/arXiv.2203.12647

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