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 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.

Batch Gating for Data Association in Monocular SLAM

2013 ◽  
Vol 319 ◽  
pp. 295-301
Author(s):  
Edmundo Guerra ◽  
Rodrigo Munguia ◽  
Yolanda Bolea ◽  
Antoni Grau
Keyword(s):  
Search Algorithm ◽  
Data Association ◽  
Real Data ◽  
Single Camera ◽  
Data Validation ◽  
Compatibility Test ◽  
Monocular Slam ◽  
Validation Technique

This work describes the development and implementation of a single-camera SLAM system, introducing a novel data validation algorithm. A 6-DOF monocular SLAM method developed is based on the Delayed Inverse-Depth (DI-D) Feature Initialization, with the addition of a new data association batch validation technique, the Highest Order Hypothesis Compatibility Test, HOHCT. The DI-D initializes new features in the system defining single hypothesis for the initial depth of features by stochastic triangulation. The HOHCT is based on evaluation of statistically compatible hypotheses, and search algorithm designed to exploit the Delayed Inverse-Depth technique characteristics. Experiments with real data are presented in order to validate the performance of the system.

Three-Dimensional Motion and Structure Estimation Using Inertial Sensors and Computer Vision for Augmented Reality

2002 ◽  
Vol 11 (5) ◽  
pp. 474-492 ◽  
Author(s):  
Lin Chai ◽  
William A. Hoff ◽  
Tyrone Vincent
Keyword(s):  
Augmented Reality ◽  
Inertial Sensors ◽  
A Priori ◽  
3D Structure ◽  
Three Dimensional ◽  
Real Data ◽  
Single Camera ◽  
Extended Kalman Filters ◽  
Structure Estimation ◽  
Better Than

A new method for registration in augmented reality (AR) was developed that simultaneously tracks the position, orientation, and motion of the user's head, as well as estimating the three-dimensional (3D) structure of the scene. The method fuses data from head-mounted cameras and head-mounted inertial sensors. Two extended Kalman filters (EKFs) are used: one estimates the motion of the user's head and the other estimates the 3D locations of points in the scene. A recursive loop is used between the two EKFs. The algorithm was tested using a combination of synthetic and real data, and in general was found to perform well. A further test showed that a system using two cameras performed much better than a system using a single camera, although improving the accuracy of the inertial sensors can partially compensate for the loss of one camera. The method is suitable for use in completely unstructured and unprepared environments. Unlike previous work in this area, this method requires no a priori knowledge about the scene, and can work in environments in which the objects of interest are close to the user.

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 A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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.

Bag-of-words-driven, single-camera simultaneous localization and mapping

2010 ◽  
Vol 28 (2) ◽  
pp. 204-226 ◽  
Author(s):  
Tom Botterill ◽  
Steven Mills ◽  
Richard Green
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Single Camera ◽  
Bag Of Words ◽  
Localization And Mapping

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.

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