scholarly journals A Novel Approach to Global Positioning System Accuracy Assessment, Verified on LiDAR Alignment of One Million Kilometers at a Continent Scale, as a Foundation for Autonomous Driving Safety Analysis

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5691
Author(s):  
Janusz Bedkowski ◽  
Hubert Nowak ◽  
Blazej Kubiak ◽  
Witold Studzinski ◽  
Maciej Janeczek ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Accuracy Assessment ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Positioning System ◽  
Mobile Mapping ◽  
Gps Positioning ◽  
Data Alignment ◽  
Novel Approach ◽  
Global Positioning

This paper concerns a new methodology for accuracy assessment of GPS (Global Positioning System) verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of an autonomous driving vehicle positioning within a lane on the road is one of the key safety considerations and the main focus of this paper. The accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high-definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. The goal is to align all the available LiDAR car trajectories to confirm the of accuracy of GNSS+INS (Global Navigation Satellite System + Inertial Navigation System). For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, Velodyne HDL32 LiDAR) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32,785 trajectories with a total length of 1,159,956.9 km and a total of 186.4 × 109 optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016–2019 period. The alignment improves the trajectories; thus the final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment, focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99% of the assessed data satisfy the safety requirements (driving within lanes of 3.6 m) for Mid-Size (width 1.85 m, length 4.87 m) vehicles and 95% for Six-Wheel Pickup (width 2.03–2.43 m, length 5.32–6.76 m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at the global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

scholarly journals A Novel Approach to Global Positioning System Accuracy Assessment, Verified on LiDAR Alignment of One Million Kilometers at a Continent Scale, as a Foundation for Autonomous Driving Safety Analysis

2021 ◽  
Author(s):  
Janusz Bedkowski ◽  
Hubert Nowak ◽  
Blazej Kubiak ◽  
Witold Studzinski ◽  
Maciej Janeczek ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Accuracy Assessment ◽  
Autonomous Driving ◽  
Safety Analysis ◽  
Driving Safety ◽  
Positioning System ◽  
Mobile Mapping ◽  
Data Alignment ◽  
Novel Approach ◽  
Global Positioning

This paper concerns a new methodology for accuracy assessment of global positioning system verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of GPS (Global Positioning System) positioning of an autonomous driving vehicle within a lane on the road is one of the key safety considerations. Safety is addressed as a geometry of the problem, where the aim is to maintain knowledge that the vehicle (its bounding box) is within its lane. Accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. For this reason, a new methodology is proposed. Methodology is composed of six elements: 1) Mobile mapping system minimal setup, 2) Global positioning data processing, 3) LiDAR data processing, 4) Alignment algorithm, 5) Accuracy assessment confirmation and 6) Autonomous driving safety analysis. The research challenge is to assess positioning accuracy of moving cars taking into account the constraints of the coverage of limited access highways in the United States of America. The available coverage limits the possibility of repeatable measurements and introduces an important challenge being the lack the ground truth data. State-of-the-art methods are not applicable for this particular application, therefore a novel approach is proposed. The method is to align all the available LiDAR car trajectories to confirm the GNSS+INS (Global Navigation Satellite System + Inertial Navigation System) accuracy. For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. SLAM implementation used state-of-the-art observation equations and the Weighted Non-Linear Least Square optimization technique that enables integration of the required constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, LiDAR Velodyne HDL32) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32785 trajectories with total length of 1,159,956.9~km and of total $186.4*10^{9}$~optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016--2019 period. It is demonstrated that the alignment improves the trajectories, thus final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99\% of the assessed data satisfies the safety requirements (driving within lanes of 3.6~m) for Mid-Size (width 1.85~m, length 4.87~m) vehicle and 95\% for 6-Wheel Pickup (width 2.03--2.43~m, length 5.32--6.76~m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

In-Between Mobile Maps and Media: Movement

2016 ◽  
Vol 18 (4) ◽  
pp. 320-335 ◽  
Author(s):  
Clancy Wilmott
Keyword(s):  
Global Positioning System ◽  
Positioning System ◽  
Mobile Mapping ◽  
Spatial Experience ◽  
Global Positioning ◽  
Reductive Process ◽  
Geocoded Data ◽  
The Relationship ◽  
Mobile Maps ◽  
Firsthand Experience

This article moves beyond the textuality of the map to focus on the way in which mobile mapping is constructed discursively, semiotically, and experientially. It centers on the autoethnographic and reflective experience of the researcher analyzing video and Global Positioning System (GPS) recordings of walking interviews, during which the interviewees conversed about, and engaged in, mobile mapping practices. This reductive process can be considered in light of its re-presentation to the researcher for analytical purposes—a ghostly abstraction of a past spatial experience. The article considers the manifold hauntings stirred in the process of abstraction and the creation of multiple layers of experience: that of the firsthand experience of the walking interview and that of the secondhand analysis of the video and geocoded data. The discrepancy between firsthand movement and secondhand analysis underscores questions about the relationship between mobile maps, representation, and movement and about those epistemologies and ontologies that haunt the interstices between individual records.

EMERGENCY EVACUATION SIMULATION VISUALIZED BY MOBILE MAPPING SYSTEM

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Osamu Tsujihara ◽  
Hideyuki Ito ◽  
Terumasa Okamoto
Keyword(s):  
Global Positioning System ◽  
Emergency Evacuation ◽  
Positioning System ◽  
Mobile Mapping ◽  
Evacuation Simulation ◽  
Camera Angle ◽  
Mapping System ◽  
Global Positioning ◽  
Mobile Mapping System ◽  
Serial Images

Recently, studies on evacuation simulations have drawn up scenarios of evacuation under various situations. In this study, a system is proposed to show the results of evacuation simulations for disasters such as tsunamis and fires, with maps and serial images taken by MMS (Mobile Mapping System). The all-around view camera, angle meter, and GPS (Global Positioning System) antenna are mounted on a moving object, such as a car, in MMS. The serially-taken images can be related to GIS (Geographic Information System). Users can not only virtually experience the evacuation but also find the dangerous places by observing the 360-degree surrounding image.

Experimental Study on Accuracy of GPS Positioning

2012 ◽  
Vol 263-266 ◽  
pp. 346-349 ◽  
Author(s):  
Hong Shi ◽  
Dong Hai Qiao
Keyword(s):  
Global Positioning System ◽  
Small Area ◽  
Positioning System ◽  
Gps Receiver ◽  
Positioning Accuracy ◽  
Gps Positioning ◽  
Global Positioning ◽  
Open Ground ◽  
Geophysical Measurement ◽  
Measured Distance

Geophysical measurement relies on the positioning accuracy of GPS (global positioning system). Usually the positioning accuracy is area dependent. This paper uses a commercially available GPS receiver to verify its positioning accuracy with practical measurement in a small area. With a measurement setup in an open ground, the results show that even for the fixed point, the GPS measured positioning error of about 0.234 meter could be observed for a period of time. Of 12 GPS measured distance errors, only one is about 5.7 meters, all others are within the range of 3-5 meters of GPS receiver specification.

Constructing common height maps with various entropy-based similarity metrics and utilizing layering method for heterogeneous robot teams

2020 ◽  
Vol 47 (6) ◽  
pp. 889-902
Author(s):  
Mehmet Caner Akay ◽  
Hakan Temeltaş
Keyword(s):  
Global Positioning System ◽  
Shannon Entropy ◽  
Similarity Metrics ◽  
Positioning System ◽  
Content Type ◽  
Robot Teams ◽  
Novel Approach ◽  
Leibler Divergence ◽  
Global Positioning ◽  
Jensen Shannon Divergence

Purpose Heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking and exploration. Exploration missions with heterogeneous robot teams (HeRTs) should acquire a common map for understanding the surroundings better. The purpose of this paper is to provide a unique approach with cooperative use of agents that provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, this method is suitable for real-time applications and autonomous path planning for exploration. Design/methodology/approach Lidar odometry and mapping and various similarity metrics such as Shannon entropy, Kullback–Leibler divergence, Jeffrey divergence, K divergence, Topsoe divergence, Jensen–Shannon divergence and Jensen divergence are used to construct a common height map of the environment. Furthermore, the authors presented the layering method that provides more accuracy and a better understanding of the common map. Findings In summary, with the experiments, the authors observed features located beneath the trees or the roofed top areas and above them without any need for global positioning system signal. Additionally, a more effective common map that enables planning trajectories for both vehicles is obtained with the determined similarity metric and the layering method. Originality/value In this study, the authors present a unique solution that implements various entropy-based similarity metrics with the aim of constructing common maps of the environment with HeRTs. To create common maps, Shannon entropy–based similarity metrics can be used, as it is the only one that holds the chain rule of conditional probability precisely. Seven distinct similarity metrics are compared, and the most effective one is chosen for getting a more comprehensive and valid common map. Moreover, different from all the studies in literature, the layering method is used to compute the similarities of each local map obtained by a HeRT. This method also provides the accuracy of the merged common map, as robots’ sight of view prevents the same observations of the environment in features such as a roofed area or trees. This novel approach can also be used in global positioning system-denied and closed environments. The results are verified with experiments.

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