scholarly journals Monocular Visual SLAM Based on a Cooperative UAV–Target System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3531 ◽  
Author(s):  
Juan-Carlos Trujillo ◽  
Rodrigo Munguia ◽  
Sarquis Urzua ◽  
Edmundo Guerra ◽  
Antoni Grau
Keyword(s):  
A Priori ◽  
Target Position ◽  
Real Data ◽  
Good Alternative ◽  
Lyapunov Theory ◽  
Target System ◽  
Operational Environment ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Reliable Solution

To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV–Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.

scholarly journals Monocular SLAM for Visual Odometry: A Full Approach to the Delayed Inverse-Depth Feature Initialization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-26 ◽  
Author(s):  
Rodrigo Munguía ◽  
Antoni Grau
Keyword(s):  
Sensory Input ◽  
A Priori ◽  
Real Data ◽  
Visual Odometry ◽  
Visual Features ◽  
Single Camera ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Freely Moving ◽  
Depth Feature

This paper describes in a detailed manner a method to implement a simultaneous localization and mapping (SLAM) system based on monocular vision for applications of visual odometry, appearance-based sensing, and emulation of range-bearing measurements. SLAM techniques are required to operate mobile robots ina prioriunknown environments using only on-board sensors to simultaneously build a map of their surroundings; this map will be needed for the robot to track its position. In this context, the 6-DOF (degree of freedom) monocular camera case (monocular SLAM) possibly represents the harder variant of SLAM. In monocular SLAM, a single camera, which is freely moving through its environment, represents the sole sensory input to the system. The method proposed in this paper is based on a technique called delayed inverse-depth feature initialization, which is intended to initialize new visual features on the system. In this work, detailed formulation, extended discussions, and experiments with real data are presented in order to validate and to show the performance of the proposal.

scholarly journals Vision-based SLAM system for MAVs in GPS-denied environments

2017 ◽  
Vol 9 (4) ◽  
pp. 283-296 ◽  
Author(s):  
Sarquis Urzua ◽  
Rodrigo Munguía ◽  
Antoni Grau
Keyword(s):  
Sensory Input ◽  
Low Cost ◽  
Real Data ◽  
Micro Aerial Vehicle ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Payload Capacity ◽  
The World ◽  
Aerial Vehicle ◽  
Metric Scale

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.

scholarly journals Minimalistic approach for monocular SLAM system applied to micro aerial vehicles in GPS-denied environments

2018 ◽  
Vol 40 (16) ◽  
pp. 4345-4357 ◽  
Author(s):  
Sarquis Urzua ◽  
Rodrigo Munguía ◽  
Emmanuel Nuño ◽  
Antoni Grau
Keyword(s):  
Low Cost ◽  
Sufficient Conditions ◽  
Real Data ◽  
Simplified Model ◽  
Micro Aerial Vehicles ◽  
Aerial Vehicles ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Theoretical Results ◽  
Metric Scale

In this work, a novel monocular simultaneous localization and mapping (SLAM) system with application to micro aerial vehicles is proposed. The main difference with respect to previous approaches is that a barometer is used as a unique sensory aid for incorporating altitude information into the system in order to recover an absolute metric scale. First, an observability analysis of a simplified model of a monocular SLAM system is developed. From this analysis, several theoretical results are derived. Among others, one important result is related to the fact that the metric scale can become observable when measurements of altitude are included in the system. In this case, sufficient conditions for observability are presented. The design of the proposed method is based on these theoretical results. Simulations and experiments with real data are presented to validate the proposed approach. The results confirm that the metric scale can be retrieved by including altitude measurements in the system. It is also shown that the proposed method can be practically implemented, using low-cost sensors, to perform visual-based navigation in GPS-denied environments.

scholarly journals Penalized partial least squares for pleiotropy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Camilo Broc ◽  
Therese Truong ◽  
Benoit Liquet
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Association Studies ◽  
A Priori ◽  
Simulated Data ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Multiple Traits ◽  
Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

scholarly journals Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
N. Vanello ◽  
E. Ricciardi ◽  
L. Landini
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Similarity Measures ◽  
Real Data ◽  
Fmri Data ◽  
Order Selection ◽  
Functional Magnetic Resonance ◽  
Model Order Selection ◽  
Model Order ◽  
Independent Components

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

scholarly journals Penentu Jarak Tembak Berdasarkan Kerusakan Proyektil Timah Tanpa Jacket untuk Senjata Api Genggam Jenis Revolver S&W Kaliber .38 Spesial pada Target Tulang

2015 ◽  
Vol 17 (3) ◽  
pp. 135
Author(s):  
Sudibyo Sudibyo
Keyword(s):  
Target Location ◽  
Target Position ◽  
Real Data ◽  
Criminal Cases ◽  
Shape Coefficient ◽  
A Value ◽  
Technical Specifications ◽  
Table Data ◽  
Throw Distance ◽  
Test Firing

Abstract This study aims to predict the shooting range based on damage the type of lead a projectile without jacket caliber.38 special fired from handguns kinds brand Revolver S & W caliber .38 specials. Based on the phenomenon of criminal cases of abuse handguns types Revolver and the fact that real data it was found that 8% of the amount of lead projectiles without jacket as forensic evidence, the condition has broken the deformed moderate to severe.         The study was conducted at the Police Forensic Laboratory experimental method test-fired in the shooting box at short throw distance range of 0.5 to 6 meters , where the bone is positioned at the target position changes location every 0.5 meters, so the total number of shots is 12 times shot on 12 position target location, and finally obtained 12 variations of deformation projectile shot results.        Stages test firing conducted through three stages as follows: 1). Phase sample preparation equipment and materials firearms, bullets and target bone. 2). Phase shooting target accurately. 3). Stages of deformation measurements and weighing projectile, arranged in the form of table data.        Material samples of bullet used was the type of lead bullets without jacket caliber .38 special with technical specifications diameter of projectile 9.09 mm (real 9.05 mm), length of projectile 17.90 mm (real 18.61 mm), projectile material lead antimony, projectile weight of 10.25 grams, muzzle velocity (initial) 265 m / sec, rounded nose shape, coefficient of form C = 2, the ballistic coefficient i = 0,9 effective range or the distance accurately of 25 meters.        Material samples of bone were used as target is 1694 SR veal ribs with bone hardness values (87 ± 1.5) shore, is used for the calibration test firing, a human skull age adults (≥ 35 years) with a value of hardness (78 ± 6 ) shore, is used as the target subjects of research, human ribs (costal C-3 / C-6) adult (≥ 35 years) with a value of hardness (69 ± 19.5) shore, is used as the target subjects of research. Keywords : deformation; projectiles; bones

scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

scholarly journals A Robust Localization System for Inspection Robots in Sewer Networks

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4946 ◽  
Author(s):  
David Alejo ◽  
Fernando Caballero ◽  
Luis Merino
Keyword(s):  
Cross Sections ◽  
Prior Information ◽  
Real Data ◽  
Localization System ◽  
Sewer Network ◽  
Localization And Mapping ◽  
Localization Errors ◽  
Robust Localization ◽  
Monte Carlo Localization ◽  
Inspection Robots

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.

scholarly journals A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

2020 ◽  
Vol 12 (18) ◽  
pp. 2923
Author(s):  
Tengfei Zhou ◽  
Xiaojun Cheng ◽  
Peng Lin ◽  
Zhenlun Wu ◽  
Ensheng Liu
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Real Data ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
General Point ◽  
Exterior Orientation ◽  
Self Calibration ◽  
Laser Scanners ◽  
Orientation Parameters

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

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