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Yang M, Sun X, Jia F, Rushworth A, Dong X, Zhang S, Fang Z, Yang G, Liu B. Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review. Polymers (Basel) 2022; 14:polym14102019. [PMID: 35631899 PMCID: PMC9143447 DOI: 10.3390/polym14102019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 02/04/2023] Open
Abstract
Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.
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Affiliation(s)
- Mengshen Yang
- Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (M.Y.); (F.J.); (B.L.)
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China;
- Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo 315201, China
| | - Xu Sun
- Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (M.Y.); (F.J.); (B.L.)
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315100, China
- Correspondence: (X.S.); (A.R.); (G.Y.)
| | - Fuhua Jia
- Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (M.Y.); (F.J.); (B.L.)
| | - Adam Rushworth
- Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (M.Y.); (F.J.); (B.L.)
- Correspondence: (X.S.); (A.R.); (G.Y.)
| | - Xin Dong
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, Nottingham NG7 2RD, UK;
| | - Sheng Zhang
- Ningbo Research Institute, Zhejiang University, Ningbo 315100, China;
| | - Zaojun Fang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China;
- Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo 315201, China
| | - Guilin Yang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China;
- Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo 315201, China
- Correspondence: (X.S.); (A.R.); (G.Y.)
| | - Bingjian Liu
- Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (M.Y.); (F.J.); (B.L.)
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Long N, Wang K, Cheng R, Hu W, Yang K. Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2019; 90:044102. [PMID: 31042998 DOI: 10.1063/1.5093279] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
It is very difficult for visually impaired people to perceive and avoid obstacles at a distance. To address this problem, the unified framework of multiple target detection, recognition, and fusion is proposed based on the sensor fusion system comprising a low-power millimeter wave (MMW) radar and an RGB-Depth (RGB-D) sensor. In this paper, the Mask R-CNN and the single shot multibox detector network are utilized to detect and recognize the objects from color images. The obstacles' depth information is obtained from the depth images using the MeanShift algorithm. The position and velocity information on the multiple target is detected by the MMW radar based on the principle of a frequency modulated continuous wave. The data fusion based on the particle filter obtains more accurate state estimation and richer information by fusing the detection results from the color images, depth images, and radar data compared with using only one sensor. The experimental results show that the data fusion enriches the detection results. Meanwhile, the effective detection range is expanded compared to using only the RGB-D sensor. Moreover, the data fusion results keep high accuracy and stability under diverse range and illumination conditions. As a wearable system, the sensor fusion system has the characteristics of versatility, portability, and cost-effectiveness.
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Affiliation(s)
- Ningbo Long
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China
| | - Kaiwei Wang
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China
| | - Ruiqi Cheng
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China
| | - Weijian Hu
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China
| | - Kailun Yang
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China
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