Improving Positioning Accuracy of the Mobile Laser Scanning in GPS-Denied Environments: An Experimental Case Study

Liu, Wanli; Li, Zhixiong; Sun, Shuaishuai; Malekian, Reza; Ma, Zhenjun; Li, Weihua

doi:10.1109/jsen.2019.2929142

Cited by 19 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In combination with the position data, IMU allows to transform the point data obtained by the MLS local frame into the ground-centric-ground-fixed system. Hence, all points are projected into a common framework and any non-compensated error from the IMU will have a direct effect on the geometrical quality of the point cloud (Liu et al 2019). The level of accuracy of the GNSS/IMU navigation system is related to the signal detection quality of the GNSS, especially if it is composed by low-cost sensors (Chang et al 2020).…”

Section: Localization and Mapping Systemsmentioning

confidence: 99%

“…Datadriven methods may be directly used to fix point clouds data starting from ground truths and using multiple available correction algorithms. On the other hand, model-driven approaches set up mathematical models for the MLS systems and analyse the error factors to calibrate the biases (Liu et al 2019). Furthermore, Simultaneous Localization and Mapping (SLAM) algorithms have been investigated in robotics in the past years.…”

Section: Localization and Mapping Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile 3D scan LiDAR: a literature review

Stefano

Chiappini

Gorreja

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

This paper, by critically reviewing different years (from 2010 to 2020) of research activities performed with Mobile Laser Scanning system, aims to review existing systems and how they are exploited in multifaceted domains. To such extent, the work defines five field domains where Mobile Laser Scanning have been used: Built and urban environment, Cultural heritage and Archaeology, Underground environment, Environmental monitoring, Forestry and Agriculture. Besides, this paper sheds the light on the pros and cons for each domain field, providing useful guidelines for those researchers involved in three-dimensional data collection with innovative systems. To achieve these purposes, research papers, were analysed, mainly considering geosciences related journals. The comparison among them revealed that, despite the incredible potential of Mobile Mapping System, the human intervention is still mandatory, and post-processing actions are needed to achieve the desired results, regardless the domain field. Moreover, our study provides insight into the technical and methodological limitations that raise a general scepticism on Mobile Mapping System for three-dimensional surveying, highlighting that in most of cases supplementary data are required to make the final result trustworthy. Such obstacles, hampering Mobile Laser Scanning diffusion, point towards unexplored areas for further investigations, serving as useful guidelines for future research directions.

show abstract

Section: Localization and Mapping Systemsmentioning

confidence: 99%

Section: Localization and Mapping Systemsmentioning

confidence: 99%

Mobile 3D scan LiDAR: a literature review

Stefano

Chiappini

Gorreja

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…(3) Resource allocation in the information group perception system: the promotion of crowd sensing real-time detection network also faces resource constraints, such as bandwidth, frequency range, and power. In order to complete a specific perception task, it is necessary to model and predict resource allocation, but due to the uncertainty of the number of users, the crowd sensing system makes the modeling process more difficult [19].…”

Section: Group Intelligence Perception Urban Healthymentioning

confidence: 99%

Crowd Sensing Urban Healthy Street Monitoring Based on Mobile Positioning System

Jiang

Sun

Sha

2021

Mobile Information Systems

View full text Add to dashboard Cite

With the rapid development of social economy, the importance of ecological civilization is increasing day by day. The level of environmental monitoring will directly affect the control of total pollution sources and the evaluation of environmental quality. Crowd sensing is to use the group computing power of users with smart devices to quickly collect surrounding multidimensional data, analyze and calculate based on these massive perception data, and then dig out group behavior patterns and other regular information. In order to meet the basic requirements of environmental protection work put forward by the government in the new era, this paper proposes mobile positioning and crowd sensing technologies, explains the distribution map method and adaptive weighting data fusion related algorithms, and designs and develops a mobile positioning system based on mobile positioning. The system of crowd sensing of urban healthy street monitoring is designed, and then we use this PM2.5 monitoring system to monitor the relevant environmental data of a street in Shanghai and use normalization to process the experimental data. The experimental data and statistical results show that this performance of the system is good, and the accuracy of image PM2.5 monitoring reaches 90%–95%. More than 90% of street residents are also very satisfied with this system, believing that it can play a role in supervising urban healthy street.

show abstract

“…e velocity and position can be obtained by equations (5) and (6), respectively. In the stepping phase, the EKF can reduce the error in the velocity and position, but the influence of acceleration and angular velocity noise on the measurement noise cannot be ignored.…”

Section: Pedestrian Positioning Algorithmmentioning

confidence: 99%

“…e cumulative error characteristics of inertial navigation integration greatly impact the positioning accuracy of a pedestrian in environments lacking GPS. erefore, it is very important to improve the pedestrian attitude and positioning accuracy without GPS signals [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Liu

et al. 2020

Complexity

View full text Add to dashboard Cite

The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

show abstract

Improving Positioning Accuracy of the Mobile Laser Scanning in GPS-Denied Environments: An Experimental Case Study

Cited by 19 publications

References 33 publications

Mobile 3D scan LiDAR: a literature review

Mobile 3D scan LiDAR: a literature review

Crowd Sensing Urban Healthy Street Monitoring Based on Mobile Positioning System

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Contact Info

Product

Resources

About