A bathymetric mapping and SLAM dataset with high-precision ground truth for marine robotics

2021 ◽  
pp. 027836492110447
Author(s):  
Kristopher Krasnosky ◽  
Christopher Roman ◽  
David Casagrande
Keyword(s):  
High Precision ◽  
Ground Truth ◽  
Underwater Vehicles ◽  
Short Baseline ◽  
Positioning Systems ◽  
Navigation Solution ◽  
Long Baseline ◽  
Localization And Mapping ◽  
Wide Range ◽  
Rtk Gps

In recent years, sonar systems for surface and underwater vehicles have increased in resolution and become significantly less expensive. As such, these systems are viable at a wide range of price points and are appropriate for a broad set of applications on surface and underwater vehicles. However, to take full advantage of these high-resolution sensors for seafloor mapping tasks an adequate navigation solution is also required. In GPS-denied environments this usually necessitates a simultaneous localization and mapping (SLAM) technique to maintain good accuracy with minimal error accumulation. Acoustic positioning systems such as ultra short baseline (USBL) and long baseline (LBL) are sometimes deployed to provide additional bounds on the navigation solution, but the positional uncertainty of these systems is often much greater than the resolution of modern multibeam or interferometric side scan sonars. As such, subsurface vehicles often lack the means to adequately ground-truth navigation solutions and the resulting bathymetic maps. In this article, we present a dataset with four separate surveys designed to test bathymetric SLAM algorithms using two modern sonars, typical underwater vehicle navigation sensors, and high-precision (2 cm horizontal, 10 cm vertical) real-time kinematic (RTK) GPS ground truth. In addition, these data can be used to refine and improve other aspects of multibeam sonar mapping such as ray-tracing, gridding techniques, and time-varying attitude corrections.

scholarly journals INDOOR NAVIGATION AND MAPPING: PERFORMANCE ANALYSIS OF UWB-BASED PLATFORM POSITIONING

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 549-555
Author(s):  
A. Masiero ◽  
H. Perakis ◽  
J. Gabela ◽  
C. Toth ◽  
V. Gikas ◽  
...  
Keyword(s):  
Wide Band ◽  
Indoor Navigation ◽  
Operating Conditions ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Localization And Mapping ◽  
Wide Range ◽  
Increasing Demand ◽  
Slam Algorithm ◽  
Tof Camera

Abstract. The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.

scholarly journals On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multi-year Field Tests

2021 ◽  
Author(s):  
Richard Czikhardt ◽  
Hans van der Marel ◽  
Juraj Papco ◽  
Ramon Hanssen
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
High Precision ◽  
Deformation Monitoring ◽  
Seasonal Effects ◽  
Attractive Alternative ◽  
Leg Length ◽  
Short Baseline ◽  
Wide Range ◽  
The Impact

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CR) for SAR interferometric (InSAR) deformation monitoring, datum connection, and geodetic data integration.Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven.Here we address these challenges using a multitude of short baseline experiments with four transponders and six corner reflectors deployed at test sites in the Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months.We find an average Radar Cross Section (RCS) of over 42 dBm2 within a range of up to 15 degrees of elevation misalignment, which is comparable to a triangular trihedral corner reflector with a leg length of 2.0 m. Its RCS shows temporal variations of 0.3--0.7~dBm2 (standard deviation) which is partially correlated with surface temperature changes.The precision of the InSAR phase double-differences over short baselines between a transponder and a stable reference corner reflectors is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant-range, i.e., within the range resolution of the Sentinel-1 Interferometric Wide Swath (IW) product, with a temporal variability of less than 20~cm.Comparing similar transponders from the same series, we observe distinctdifferences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

Development of an ultra short baseline–aided buoy for underwater targets localization

2019 ◽  
Vol 233 (4) ◽  
pp. 1212-1225 ◽  
Author(s):  
Francesco Fanelli ◽  
Niccolò Monni ◽  
Nicola Palma ◽  
Alessandro Ridolfi
Keyword(s):  
Measurement Errors ◽  
Autonomous Underwater Vehicles ◽  
Dynamic Modelling ◽  
Experimental Tests ◽  
Underwater Vehicles ◽  
Short Baseline ◽  
Vehicle Localization ◽  
Positioning Systems ◽  
Fusion Methods ◽  
Underwater Target

Autonomous underwater vehicles localization and navigation are challenging due to the lack of Global Positioning System underwater: alternative techniques have then to be used in order to measure the position of the vehicle. To this aim, sensor fusion methods based on acoustic positioning systems are often exploited. This article faces the study and the improvement of the localization of an underwater target through an ultra short baseline–aided buoy built by the Mechatronics and Dynamic Modelling Laboratory of the University of Florence. Such a buoy relies on an ultra short baseline device for the localization and is aided by a proper sensor set in order to compensate variations in its pose. First, a study of the underwater localization based on the ultra short baseline technique is provided. The measurement errors entailed by the buoy motion are then analyzed and preliminarily compensated, exploiting linear least squares methods. Subsequently, filtering techniques are considered with the aim to further increase the accuracy of the ultra short baseline measurements. Due to the nonlinearities of the sensors characteristics, extended Kalman filter has been used, with different models for stationary and moving targets. The solutions proposed have been validated through experimental tests conducted with MArine Robotic Tool for Archaeology autonomous underwater vehicles built by the Mechatronics and Dynamic Modelling Laboratory. The results evidence an improved vehicle localization, suggesting interesting future developments concerning both mechanical and computational solutions.

A Theoretical and Experimental Study of the Vibratory Behavior of a High-Precision Optical Positioning Table

1997 ◽  
Author(s):  
Ipek Basdogan ◽  
Thomas J. Royston ◽  
Juan Barraza ◽  
Deming Shu ◽  
Tuncer M. Kuzay
Keyword(s):  
High Precision ◽  
Dynamic Properties ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
Theoretical Models ◽  
Positioning Systems ◽  
Wide Range ◽  
Optical Table ◽  
The Individual ◽  
Optical Positioning

Abstract At the Advanced Photon Source (APS), a state-of-the-art synchrotron radiation facility at Argonne National Laboratory (ANL), high-precision optical positioning systems are needed to conduct a wide range of experiments utilizing the high-brilliance x-ray beam. The high-precision, multi-dimensional positioning capability required for these positioning systems may be compromised by vibratory motion. The vibratory dynamics of the complex kinematic joints and components that comprise these multibody structures are not easily described by simple theoretical models. A combined experimental and theoretical approach has been developed to predict the dynamic properties of the individual components and joints and of the assembled multibody system. A prototypical optical table has been analyzed as an example case.

The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises

2021 ◽  
pp. 027836492110049
Author(s):  
Jesús Morales ◽  
Ricardo Vázquez-Martín ◽  
Anthony Mandow ◽  
David Morilla-Cabello ◽  
Alfonso García-Cerezo
Keyword(s):  
Disaster Response ◽  
Three Dimensional ◽  
Ground Truth ◽  
Measurement Unit ◽  
Emergency Responders ◽  
Full Dataset ◽  
Total Path Length ◽  
Localization And Mapping ◽  
Wide Range ◽  
Detection And Localization

This article presents a collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. The sensor suite, applicable to unmanned ground vehicles (UGVs), consisted of overlapping visible light (RGB) and thermal infrared (TIR) forward-looking monocular cameras, a Velodyne HDL-32 three-dimensional (3D) lidar, as well as an inertial measurement unit (IMU) and two global positioning system (GPS) receivers as ground truth. Our mission was to collect a wide range of data from the SAR domain, including persons, vehicles, debris, and SAR activity on unstructured terrain. In particular, four data sequences were collected following closed-loop routes during the exercises, with a total path length of 5.2 km and a total time of 77 min. In addition, we provide three more sequences of the empty site for comparison purposes (an extra 4.9 km and 46 min). Furthermore, the data is offered both in human-readable format and as rosbag files, and two specific software tools are provided for extracting and adapting this dataset to the users’ preference. The review of previously published disaster robotics repositories indicates that this dataset can contribute to fill a gap regarding visual and thermal datasets and can serve as a research tool for cross-cutting areas such as multispectral image fusion, machine learning for scene understanding, person and object detection, and localization and mapping in unstructured environments. The full dataset is publicly available at: www.uma.es/robotics-and-mechatronics/sar-datasets .

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

scholarly journals Separate Universe calibration of the dependence of halo bias on cosmic web anisotropy

2020 ◽  
Vol 499 (3) ◽  
pp. 4418-4431 ◽  
Author(s):  
Sujatha Ramakrishnan ◽  
Aseem Paranjape
Keyword(s):  
Dark Matter ◽  
High Precision ◽  
Gaussian Distribution ◽  
Initial Perturbation ◽  
Analytical Framework ◽  
Web Environment ◽  
Wide Range ◽  
Galaxy Assembly ◽  
Very High ◽  
Good Agreement

ABSTRACT We use the Separate Universe technique to calibrate the dependence of linear and quadratic halo bias b1 and b2 on the local cosmic web environment of dark matter haloes. We do this by measuring the response of halo abundances at fixed mass and cosmic web tidal anisotropy α to an infinite wavelength initial perturbation. We augment our measurements with an analytical framework developed in earlier work that exploits the near-lognormal shape of the distribution of α and results in very high precision calibrations. We present convenient fitting functions for the dependence of b1 and b2 on α over a wide range of halo mass for redshifts 0 ≤ z ≤ 1. Our calibration of b2(α) is the first demonstration to date of the dependence of non-linear bias on the local web environment. Motivated by previous results that showed that α is the primary indicator of halo assembly bias for a number of halo properties beyond halo mass, we then extend our analytical framework to accommodate the dependence of b1 and b2 on any such secondary property that has, or can be monotonically transformed to have, a Gaussian distribution. We demonstrate this technique for the specific case of halo concentration, finding good agreement with previous results. Our calibrations will be useful for a variety of halo model analyses focusing on galaxy assembly bias, as well as analytical forecasts of the potential for using α as a segregating variable in multitracer analyses.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

Sign in / Sign up

Export Citation Format

Share Document