Incorporating SLAM and mobile sensing for indoor CO2 monitoring and source position estimation

Yang, Yuan; Liu, Jiang; Wang, Wei; Cao, Yu; Li, Heng

doi:10.1016/j.jclepro.2020.125780

Cited by 20 publications

(9 citation statements)

References 37 publications

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“…All transformed scan points have formed new vertical scan S NT which has further combined with the horizontal scan S H to form the complete scan S F of the surveying system at any particular time as shown in Eq (6).…”

Section: Plos Onementioning

confidence: 99%

“…In domestic applications, the SLAM based mobile robots are more popular in multiple tasks such as educational, cleaning and other assistive applications. A research group produced an innovative solution to estimate the indoor emissions of CO 2 using SLAM equipped rover navigation [ 6 ]. In order to improve the accuracy of the SLAM technique, the trend of fusing 2D and 3D laser scans with the sequential stereo and monocular camera images has become the favorite practice among the robotics researchers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compact rover surveying and laser scanning for BIM development

Jafri,

Hussain,

Ahmed

et al. 2024

PLoS ONE

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This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compact rover surveying and laser scanning for BIM development

Jafri,

Hussain,

Ahmed

et al. 2024

PLoS ONE

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“…The automated robots effectively addressed the issue of data uploading in stationary sensing owing to the limited wireless coverage in large space. More recently, Yang et al (2021b) introduced a mobile sensing platform for prompt detecting and positioning of pollutant sources. As compared to stationary sensing with multiple sensors, the mobile sensing delivered more accurate and highly granular contaminant detections.…”

Section: Existing Workmentioning

confidence: 99%

AlphaMobileSensing: A virtual testbed for mobile environmental monitoring

Zhou

Zhong

et al. 2023

Build. Simul.

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Environmental monitoring plays a critical role in creating and maintaining a comfortable, productive, and healthy environment. Built upon the advancements of robotics and data processing, mobile sensing demonstrates its potential to address problems regarding cost, deployment, and resolution that stationary monitoring encounters, which therefore has attracted increasing research attentions recently. To facilitate mobile sensing, two key algorithms are needed: the field reconstruction algorithm and the route planning algorithm. The field reconstruction algorithm is to reconstruct the entire environment field from spatially-and temporally-discrete measurements collected by the mobile sensors. The route planning algorithm is to instruct the mobile sensors where the mobile sensor needs to move to for the next measurements. The performance of mobile sensors highly depends on these two algorithms. However, developing and testing those algorithms in the real world is expensive, challenging, and time-consuming. To address these issues, we proposed and implemented an open-source virtual testbed, AlphaMobileSensing, that can be used to develop, test, and benchmark mobile sensing algorithms. AlphaMobileSensing aims to help users more easily develop and test the field reconstruction and route planning algorithms for mobile sensing solutions, without worrying about hardware fault, test accidents (such as collision during the test), etc. The separation of concerns can significantly reduce the cost of developing software solutions for mobile sensing. For versatility and flexibility, AlphaMobileSensing was wrapped up using the standardized interface of OpenAI Gym, and it also provides an interface for loading physical fields that were generated by numerical simulations as virtual test sites to perform mobile sensing and retrieving monitoring data. We demonstrated applications of the virtual testbed by implementing and testing algorithms for physical field reconstruction in both static and dynamic indoor thermal environments. AlphaMobileSensing provides a novel and flexible platform to develop, test, and benchmark mobile sensing algorithms more easily, conveniently, and efficiently.AlphaMobileSensing is open sourced at https://github.com/kishuqizhou/AlphaMobileSensing.

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“…Another study by Y. Yang et al (2021) evaluates both stationary sensing (CO2 sensors) and mobile sensing (robot with CO2 sensors) in real-world experiments conducted in a laboratory room. According to the findings of this study, mobile sensing has a greater sensitivity to detecting CO 2 sources than stationary sensing.…”

Section: Wireless Sensors Technologymentioning

confidence: 99%

Smart classroom for university students: Improving air quality by integrating wireless sensors in Malaysia

Wei

Ahmad

2022

ARTEKS J. Tek. Arsit.

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In 2020, the COVID-19 pandemic has had a huge impact on the education system in Malaysia, especially at higher education level. A number of public or private universities have carried out online learning classes due to the Covid-19 pandemic and avoid the spread of the virus among university students. Face to-face learning can not be implemented because the learning efficiency in class management decreased. Thus, this study discussed the integration of wireless sensors within the classroom, focusing on improvement of ventilation system and learning efficiency in classroom design. As a result, the goals of this research are to identify the types of wireless sensors that can be used to improve the ventilation system in classroom design, to identify the mechanisms of wireless sensors that can be used to improve the ventilation system in classroom design, and to propose design guidelines for using wireless sensors to improve the ventilation system and learning efficiency in classroom design. Reviews on related topics such as wireless sensor technology and smart classroom will be made via online searching articles and journals. Acquisition of primary data and secondary data will be done via case study of smart classroom from relevant articles and journals.

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Incorporating SLAM and mobile sensing for indoor CO2 monitoring and source position estimation

Cited by 20 publications

References 37 publications

Compact rover surveying and laser scanning for BIM development

Compact rover surveying and laser scanning for BIM development

AlphaMobileSensing: A virtual testbed for mobile environmental monitoring

Smart classroom for university students: Improving air quality by integrating wireless sensors in Malaysia

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