As global concern over environmental issues intensifies, air quality monitoring has become a focal point worldwide. Traditional air quality monitoring methods, limited by fixed monitoring points, struggle with detailed and dynamic air quality control. Air quality monitoring robots based on SLAM (Simultaneous Localization and Mapping) technology emerge as a novel solution, revolutionizing the field of environmental monitoring with their mobility and flexibility. This paper reviews the application of SLAM technology in air quality monitoring robots, covering its basic principles, development history, and practical environmental monitoring applications. It also details the design elements, sensor integration, and real-world case studies of these robots. The paper identifies current technological challenges, including data accuracy and reliability, navigation in complex environments, and system real-time and autonomy issues. Finally, it anticipates future technological trends, including the application of deep learning in SLAM, development of high-precision, low-cost sensors, and expansion into various application fields, aiming to provide valuable insights for researchers and practitioners in related areas.

Topics
Quality assurance, Air pollution, Deep learning, Robotics, Geometrical optics, Review
1.
GBD 2015 Risk Factors Collaborators Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015
:
A systematic analysis for the Global Burden of Disease Study 2015. Lancet, 388 (10053
) (
2016
).
2.
C.
Cadena
,
L.
Carlone
,
H.
Carrillo
,
Y.
Latif
,
D.
Scaramuzza
,
J.
Neira
, &
F.
Dellaert
.
Past, present, and future of simultaneous localization and mapping: Toward the robust perception age
.
IEEE Transactions on Robotics
,
32
(
6
),
1309
–
1332
(
2016
).
https://doi.org/10.1109/TRO.2016.2624754
Google Scholar
Crossref
Search ADS
 
3.
H.
Zhang
,
Z.
Li
,
C.
Huang
, &
J.
Wang
.
A review of air quality monitoring methods
.
Journal of Sensors.
(
2019
).
Google Scholar
 
4.
S.
Kumar
,
R.
Nishant
,
S. W.
Kim
,
Y. H.
Kim
, &
T. S.
Kim
.
A survey of air quality detection using WSN and robotics
. In
Proceedings of the 2015 International Conference on Big Data and Smart Computing
,
457
–
460
(
2015
)
Google Scholar
 
5.
L.
Ma
 and
T.
Hartmann
.
A Proposed Ontology to Support the Hardware Design of Building Inspection Robot Systems
.
Advanced Engineering Informatics
,
55
,
101851
(
2022
).
https://doi.org/10.1016/j.aei.2022.101851
Google Scholar
Crossref
Search ADS
 
6.
M.
Van Sinderen
 et al.
A Real-Time Integration of Semantic Annotations into Air Quality Monitoring Sensor Data
. In
ICSOFT (Selected Papers)
,
1447
,
98
–
113
(
2021
).
Google Scholar
 
7.
Y.
Liu
,
M.
Zhang
, &
K.
Huang
.
Deep learning applications in SLAM for air quality monitoring robots
.
IEEE Robotics and Automation Letters
,
7
(
2
),
2077
–
2084
(
2022
).
Google Scholar
 
8.
E.
Song
,
Y.
Zhu
,
J.
Zhou
, et al.
Optimal Kalman filtering fusion with cross-correlated sensor noises
.
Automatica
,
43
(
8
),
1450
–
1456
(
2007
).
https://doi.org/10.1016/j.automatica.2007.01.010
Google Scholar
Crossref
Search ADS
 
9.
A. A.
Aguileta
 et al
Multi-Sensor Fusion for Activity Recognition - A Survey
.
Sensors (Basel, Switzerland)
,
19
(
17
),
3808
. (
2019
).
https://doi.org/10.3390/s19173808
Google Scholar
Crossref
Search ADS
PubMed
 
10.
K.
Ebadi
, et al.
Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge
.
IEEE Transactions on Robotics.
(
2023
).
Google Scholar
 
11.
IEEE Standard for Information Technology. Part 1, System Application Program Interface (API)--Amendment 1--Realtime Extension [C Language] : Portable Operating System Interface (POSIX). (
2001
).
12.
Y.
Sun
.
Cloud Edge Computing for Socialization Robot Based on Intelligent Data Envelopment
.
Computers & Electrical Engineering
,
92
,
107136
(
2021
).
https://doi.org/10.1016/j.compeleceng.2021.107136
Google Scholar
Crossref
Search ADS
 
13.
J.
Guo
 et al
Study on the Autonomous Multirobot Collaborative Control System Based on Spherical Amphibious Robots
.
IEEE Systems Journal
,
15
(
4
),
4950
–
4957
, (
2021
).
https://doi.org/10.1109/JSYST.2020.3023727
Google Scholar
Crossref
Search ADS
 
14.
Y.
Zhang
, et al.
Goslam: Global optimization for consistent 3D instant reconstruction
.
Proceedings of the IEEE/CVF International Conference on Computer Vision.
(
2023
).
Google Scholar
 
15.
H.-L.
Wang
 et al
Robot SLAM with Ad Hoc Wireless Network Adapted to Search and Rescue Environments
.
Journal of Central South University
,
25
(
12
),
3033
–
3051
. (
2018
).
https://doi.org/10.1007/s11771-018-3972-8
Google Scholar
Crossref
Search ADS
 
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