TY - JOUR
T1 - Incorporating SLAM and mobile sensing for indoor CO2 monitoring and source position estimation
AU - Yang, Yuan
AU - Liu, Jiang
AU - Wang, Wei
AU - Cao, Yu
AU - Li, Heng
N1 - Funding Information:
This work was supported by the project of the National Key Research and Development Plan of China (Grant Number: 2016YFB0502103 ), the National Natural Science Foundation of China (No. 71704022 ), Natural science foundation of Jiangsu Province (No. BK20170664 ), the Fundamental Research Funds for the Central Universities (No. 2242020K40210 ), We are thankful for the financial support of the following two grants from Research Grants Council, University Grants Committee. 1) “Proactive monitoring of work-related MSD risk factors and fall risks of construction workers using wearable insoles” (PolyU 152099/18E); and 2) In search of a suitable tool for proactive physical fatigue assessment: an invasive to non-invasive approach. (PolyU 15204719/18E).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/4/1
Y1 - 2021/4/1
N2 - Indoor Air Quality (IAQ) monitoring is becoming an increasingly important topic since it critically affects occupants’ health, comfort, and safety, etc. Conventionally, monitoring sensors are distributed at stationary positions for measurement acquisitions, which cannot easily become wireless causing a high cost of infrastructure installation and maintenance. Furthermore, stationary sensing often incurs the problems of non-detection zones and low-granularity monitoring, especially in complex indoor scenarios where environmental factors are dynamic and non-convex functions. To address these critical issues, this paper proposes a mobile IAQ sensing using an automated robot equipped with one LIDAR and one CO2 sensor, to enable the prompt detection and positioning of contaminant sources (a CO2 contaminant source). Both stationary sensing (with 9 CO2 sensors) and mobile sensing (a robot with one CO2 sensor) are evaluated in real-world experiments carried out in a typical laboratory room. The spatiotemporal analysis demonstrates that the automated mobile sensing is capable of efficient and agile IAQ survey. The heat maps of the CO2 concentration illustrate that mobile sensing observes better accuracy and granularity of the pollutant detections, as mobile sensing estimates the position of the CO2 source about 1.83 m away from the ground truth position, in contrast with stationary sensing of 3.1 m. The proposed autonomous and mobile IAQ monitoring consumes much less infrastructure (one sensor instead of nine stationary sensors), implementation complexity, and amount of data for communication and processing.
AB - Indoor Air Quality (IAQ) monitoring is becoming an increasingly important topic since it critically affects occupants’ health, comfort, and safety, etc. Conventionally, monitoring sensors are distributed at stationary positions for measurement acquisitions, which cannot easily become wireless causing a high cost of infrastructure installation and maintenance. Furthermore, stationary sensing often incurs the problems of non-detection zones and low-granularity monitoring, especially in complex indoor scenarios where environmental factors are dynamic and non-convex functions. To address these critical issues, this paper proposes a mobile IAQ sensing using an automated robot equipped with one LIDAR and one CO2 sensor, to enable the prompt detection and positioning of contaminant sources (a CO2 contaminant source). Both stationary sensing (with 9 CO2 sensors) and mobile sensing (a robot with one CO2 sensor) are evaluated in real-world experiments carried out in a typical laboratory room. The spatiotemporal analysis demonstrates that the automated mobile sensing is capable of efficient and agile IAQ survey. The heat maps of the CO2 concentration illustrate that mobile sensing observes better accuracy and granularity of the pollutant detections, as mobile sensing estimates the position of the CO2 source about 1.83 m away from the ground truth position, in contrast with stationary sensing of 3.1 m. The proposed autonomous and mobile IAQ monitoring consumes much less infrastructure (one sensor instead of nine stationary sensors), implementation complexity, and amount of data for communication and processing.
KW - Autonomous mobile sensing
KW - Indoor air contaminant detection
KW - Simultaneous localization and mapping(SLAM)
KW - Spatiotemporal distribution
UR - http://www.scopus.com/inward/record.url?scp=85099643247&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.125780
DO - 10.1016/j.jclepro.2020.125780
M3 - Journal article
AN - SCOPUS:85099643247
SN - 0959-6526
VL - 291
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 125780
ER -