Evidence map›Paper›PMID 35631899›Full record

ReviewPolymers2022

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review.

Mengshen Yang, Xu Sun, Fuhua Jia, Adam Rushworth, Xin Dong, Sheng Zhang, Zaojun Fang, Guilin Yang, Bingjian Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Biosensors toward behavior detection in diagnosis of alzheimer's disease.Frontiers in bioengineering and biotechnology · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mengshen YangDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Xu SunDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.ORCID 0000-0002-2340-7095
Fuhua JiaDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Adam RushworthDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.ORCID 0000-0003-3803-7549
Xin DongDepartment of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
Sheng ZhangNingbo Research Institute, Zhejiang University, Ningbo 315100, China.ORCID 0000-0001-5599-6261
Zaojun FangNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
Guilin YangNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
Bingjian LiuDepartment of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.ORCID 0000-0001-6438-5040

Funding

Major Program of Nature Science Foundation of Zhejiang Province LD22E050007Major Special Projects of the Plan "Science and Technology Innovation 2025" in Ningbo 2021Z020National Natural Science Foundation of China 92048201
6 · The paper itself

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.

Indexed as

cameraIMULiDARodometrypolymeric sensorradarself-contained localizationsensor fusionSLAMstate estimation

Identifiers

PMID35631899
PMCPMC9143447

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.