scholarly journals Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2452 ◽  
Author(s):  
Lakshay Narula ◽  
Michael Wooten ◽  
Matthew Murrian ◽  
Daniel LaChapelle ◽  
Todd Humphreys
Keyword(s):  
Low Cost ◽  
Vertical Direction ◽  
Satellite System ◽  
Sensor Data ◽  
Limiting Factor ◽  
Automated Driving ◽  
Digital Maps ◽  
Digital Mapping ◽  
Structure And Motion ◽  
Localization And Mapping

Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps’ globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard code-phase-based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements.

scholarly journals Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment

2020 ◽  
Vol 12 (10) ◽  
pp. 1564 ◽  
Author(s):  
Kai-Wei Chiang ◽  
Guang-Je Tsai ◽  
Yu-Hua Li ◽  
You Li ◽  
Naser El-Sheimy
Keyword(s):  
Urban Areas ◽  
Satellite System ◽  
Multisensor Fusion ◽  
Automated Driving ◽  
Integrity Assessment ◽  
Localization And Mapping ◽  
Self Driving Cars ◽  
Global Navigation Satellite ◽  
3D Lidar ◽  
Ins Aiding

Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.

Environment representations for automated on-road vehicles

2018 ◽  
Vol 66 (2) ◽  
pp. 107-118 ◽  
Author(s):  
Matthias Schreier
Keyword(s):  
Sensor Data ◽  
Feature Maps ◽  
Automated Driving ◽  
Road Vehicles ◽  
Interval Maps ◽  
Digital Maps ◽  
Occupancy Grid ◽  
Abstraction Layer ◽  
Environment Model ◽  
Multi Level

AbstractOne of the key challenges of anyAutomated Driving(AD) system lies in the perception and representation of the driving environment. Data from a multitude of different information sources such as various vehicle environment sensors, external communication interfaces, and digital maps must be adequately combined to one consistentComprehensive Environment Model(CEM) that acts as a generic abstraction layer for the driving functions. This overview article summarizes and discusses different approaches in this area with a focus on metric representations of static and dynamic driving environments for on-road AD systems. Feature maps, parametric free space maps, interval maps, occupancy grid maps, elevation maps, the stixel world, multi-level surface maps, voxel grids, meshes, and raw sensor data models are presented and compared in this regard.

scholarly journals An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5201 ◽  
Author(s):  
Paul Schwarzbach ◽  
Albrecht Michler ◽  
Paula Tauscher ◽  
Oliver Michler
Keyword(s):  
Empirical Study ◽  
Low Cost ◽  
Transportation Systems ◽  
Urban Environments ◽  
Position Estimation ◽  
Sensor Data ◽  
Ieee 802.11P ◽  
Traffic Information ◽  
Automated Driving ◽  
Cooperative Positioning

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm’s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments.

scholarly journals SQUEEZEPOSENET: IMAGE BASED POSE REGRESSION WITH SMALL CONVOLUTIONAL NEURAL NETWORKS FOR REAL TIME UAS NAVIGATION

2017 ◽  
Vol IV-2/W3 ◽  
pp. 49-57 ◽  
Author(s):  
M. S. Müller ◽  
S. Urban ◽  
B. Jutzi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Satellite System ◽  
Trajectory Estimation ◽  
Localization And Mapping ◽  
Wide Range ◽  
Novel Method ◽  
Computationally Expensive ◽  
Global Navigation Satellite ◽  
Local Image

The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

scholarly journals INS-AIDED 3D LIDAR SEAMLESS MAPPING IN CHALLENGING ENVIRONMENT FOR FUTURE HIGH DEFINITION MAP

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 251-257
Author(s):  
G. J. Tsai ◽  
K. W. Chiang ◽  
N. El-Sheimy
Keyword(s):  
Low Cost ◽  
Autonomous Driving ◽  
Satellite System ◽  
Global Coordinate System ◽  
Lower Grade ◽  
High Definition ◽  
Position Information ◽  
Localization And Mapping ◽  
Parking Lot ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With advances in computing and sensor technologies, onboard systems can deal with a large amount of data and achieve real-time process continuously and accurately. In order to further enhance the performance of positioning, high definition map (HD map) is one of the game changers for future autonomous driving. Instead of directly using Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) navigation solutions to conduct the Direct Geo-referencing (DG) and acquiring 3D mapping information, Simultaneous Localization and Mapping (SLAM) relies heavily on environmental features to derive the position and attitude as well as conducting the mapping at the same time. In this research, the new structure is proposed to integrate the INS/GNSS into LiDAR Odometry and Mapping (LOAM) algorithm and enhance the mapping performance. The first contribution is using the INS/GNSS to provide the short-term relative position information for the mapping process when the LiDAR odometry process is failed. The checking process is built to detect the divergence of LiDAR odometry process based on the residual from correspondences of features and innovation sequence of INS/GNSS. More importantly, by integrating with INS/GNSS, the whole global map is located in the standard global coordinate system (WGS84) which can be shared and employed easily and seamlessly. In this research, the designed land vehicle platform includes commercial INS/GNSS integrated product as a reference, relatively low-cost and lower grade INS system and Velodyne LiDAR with 16 laser channels, respectively. The field test is conducted from outdoor to the indoor underground parking lot and the final solution using the proposed method has a significant improvement as well as building a more accurate and reliable map for future use.</p>

scholarly journals The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments

2020 ◽  
Vol 12 (11) ◽  
pp. 1732
Author(s):  
Kai-Wei Chiang ◽  
Dinh Thuan Le ◽  
Thanh Trung Duong ◽  
Rui Sun
Keyword(s):  
Low Cost ◽  
Integrated System ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Integration Scheme ◽  
Visual Data ◽  
Integrated Sensor ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Smartphone Sensors

Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.

Comparative analysis and interpretation of grace and grace-fo data

2020 ◽  
Vol 4 ◽  
pp. 101-106
Author(s):  
Konstantin Simonov ◽  
◽  
Alexander Matsulev
Keyword(s):  
Comparative Analysis ◽  
World Ocean ◽  
Satellite System ◽  
Data Archive ◽  
Space Systems ◽  
Satellite Measurements ◽  
Digital Maps ◽  
The World ◽  
Grace Mission ◽  
Anomalous State

The study is devoted to the analysis of the features of the change in the Equivalent Water Height (EWH) parameter over the geoid based on satellite measurements of space systems. The study used the GRACE and GRACE-FO satellite data archive. The assessment was carried out on Earth as a whole, including land areas and the World Ocean. Interpretation of the anomalous state of the geoenvironment is performed using digital maps of the spatial distribution of the EWH parameter based on the histogram approach and correlation analysis. Also, a comparative analysis of the studied data from the GRACE mission and data from the new GRACE-FO satellite system launched into orbit in the summer of 2018 was carried out.

scholarly journals Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2944
Author(s):  
Benjamin James Ralph ◽  
Marcel Sorger ◽  
Benjamin Schödinger ◽  
Hans-Jörg Schmölzer ◽  
Karin Hartl ◽  
...  
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Industrial Revolution ◽  
Low Cost ◽  
Material Behavior ◽  
Sensor Data ◽  
Management Tool ◽  
Special Focus ◽  
Programming Environments ◽  
Metal Processing

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

scholarly journals A V-SLAM Guided and Portable System for Photogrammetric Applications

2021 ◽  
Vol 13 (12) ◽  
pp. 2351
Author(s):  
Alessandro Torresani ◽  
Fabio Menna ◽  
Roberto Battisti ◽  
Fabio Remondino
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Motion Blur ◽  
Guidance System ◽  
Prototype System ◽  
Active Sensors ◽  
Localization And Mapping ◽  
Acquisition Speed ◽  
Mobile Mapping System ◽  
Slam Algorithm

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

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