Visual-inertial SLAM method based on optical flow in a GPS-denied environment

2018 ◽  
Vol 45 (3) ◽  
pp. 401-406 ◽  
Author(s):  
Chang Chen ◽  
Hua Zhu
Keyword(s):  
Optical Flow ◽  
Measurement Unit ◽  
Data Sets ◽  
Theoretical Derivation ◽  
Content Type ◽  
Feature Extraction Method ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
The Stability

Purpose This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles. Design/methodology/approach In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization. Findings The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets. Originality/value The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.

scholarly journals An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2004 ◽  
Author(s):  
Linlin Xia ◽  
Qingyu Meng ◽  
Deru Chi ◽  
Bo Meng ◽  
Hanrui Yang
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Feature Matching ◽  
Robot Navigation ◽  
Measurement Unit ◽  
State Estimator ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
Association State

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

2014 ◽  
Vol 41 (3) ◽  
pp. 266-274 ◽  
Author(s):  
Tianmiao Wang ◽  
Chaolei Wang ◽  
Jianhong Liang ◽  
Yicheng Zhang
Keyword(s):  
Particle Filter ◽  
Internal State ◽  
Factorization Method ◽  
Measurement Unit ◽  
Visual Slam ◽  
Vehicle Model ◽  
Theoretical Derivation ◽  
Content Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

scholarly journals VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4036 ◽  
Author(s):  
Chaofan Zhang ◽  
Yong Liu ◽  
Fan Wang ◽  
Yingwei Xia ◽  
Wen Zhang
Keyword(s):  
Feature Extraction ◽  
State Estimation ◽  
Graphics Processing Unit ◽  
Optimization Method ◽  
Measurement Unit ◽  
Processing Unit ◽  
Camera Model ◽  
Feature Extraction Method ◽  
Localization And Mapping ◽  
Tightly Coupled

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it’s the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono.

Autonomous planetary rover navigation via active SLAM

2018 ◽  
Vol 91 (1) ◽  
pp. 60-68
Author(s):  
Guoqing Li ◽  
Yunhai Geng ◽  
Wenzheng Zhang
Keyword(s):  
Feature Detection ◽  
Three Dimensional ◽  
Salient Feature ◽  
Efficient Manner ◽  
Planetary Rover ◽  
Content Type ◽  
Feature Extraction Method ◽  
Localization And Mapping ◽  
Salient Features ◽  
Exploration Mission

PurposeThis paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.Design/methodology/approachA three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.FindingsThe proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.Originality/valueThis paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

scholarly journals GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4973 ◽  
Author(s):  
Dániel Kiss-Illés ◽  
Cristina Barrado ◽  
Esther Salamí
Keyword(s):  
Frame Rate ◽  
Measurement Unit ◽  
Additional Information ◽  
Main Work ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Aerial Vehicle ◽  
Slam Algorithm ◽  
Low Frame Rate

This work presents Global Positioning System-Simultaneous Localization and Mapping (GPS-SLAM), an augmented version of Oriented FAST (Features from accelerated segment test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector (ORB)-SLAM using GPS and inertial data to make the algorithm capable of dealing with low frame rate datasets. In general, SLAM systems are successful in case of datasets with a high frame rate. This work was motivated by a scarce dataset where ORB-SLAM often loses track because of the lack of continuity. The main work includes the determination of the next frame’s pose based on the GPS and inertial data. The results show that this additional information makes the algorithm more robust. As many large, outdoor unmanned aerial vehicle (UAV) flights save the GPS and inertial measurement unit (IMU) data of the capturing of images, this program gives an option to use the SLAM algorithm successfully even if the dataset has a low frame rate.

scholarly journals The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

2019 ◽  
Vol 38 (6) ◽  
pp. 633-641 ◽  
Author(s):  
Taihú Pire ◽  
Martín Mujica ◽  
Javier Civera ◽  
Ernesto Kofman
Keyword(s):  
Global Positioning System ◽  
Mobile Robotics ◽  
Sensor Data ◽  
Measurement Unit ◽  
Direct Sunlight ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Gps Rtk ◽  
Autonomous Mobile Robotics ◽  
Multisensor Data

In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/ .

scholarly journals Ford Campus vision and lidar data set

2011 ◽  
Vol 30 (13) ◽  
pp. 1543-1552 ◽  
Author(s):  
Gaurav Pandey ◽  
James R McBride ◽  
Ryan M Eustice
Keyword(s):  
Three Dimensional ◽  
Measurement Unit ◽  
Small Scale ◽  
Data Sets ◽  
Camera System ◽  
Data Set ◽  
Mapping Algorithms ◽  
Ground Vehicle ◽  
Localization And Mapping ◽  
Vehicle Path

In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November–December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.

The COBRA fixed-wing georeferenced imagery dataset

2015 ◽  
Vol 3 (2/3) ◽  
pp. 62-71 ◽  
Author(s):  
Sajad Saeedi ◽  
Carl Thibault ◽  
Michael Trentini ◽  
Howard Li
Keyword(s):  
Ground Truth ◽  
Target Localization ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Robot Operating System ◽  
Benchmark Data ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Sensor Configuration

Purpose – The purpose of this paper is to present a localization and mapping data set acquired by a fixed-wing unmanned aerial vehicle (UAV). The data set was collected for educational and research purposes: to save time in dealing with hardware and to compare the results with a benchmark data set. The data were collected in standard Robot Operating System (ROS) format. The environment, fixed-wing, and sensor configuration are explained in detail. GPS coordinates of the fixed-wing are also available as ground truth. The data set is available for download (www.ece.unb.ca/COBRA/open_source.htm). Design/methodology/approach – The data were collected in standard ROS format. The environment, fixed-wing, and sensor configuration are explained in detail. Findings – The data set can be used for target localization and mapping. The data were collected to assist algorithm developments and help researchers to compare their results. Robotic data sets are specifically important when they are related to unmanned systems such as fixed-wing aircraft. Originality/value – The Robotics Data Set Repository (RADISH) by A. Howard and N. Roy hosts 41 well-known data sets with different sensors; however, there is no fixed-wing data set in RADISH. This work presents two data sets collected by a fixed-wing aircraft using ROS standards. The data sets can be used for target localization and SLAM.

Stereotactic radiation treatment planning and follow-up studies involving fused multimodality imaging

2004 ◽  
Vol 101 (Supplement3) ◽  
pp. 326-333 ◽  
Author(s):  
Klaus D. Hamm ◽  
Gunnar Surber ◽  
Michael Schmücking ◽  
Reinhard E. Wurm ◽  
Rene Aschenbach ◽  
...  
Keyword(s):  
Image Fusion ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Data Sets ◽  
Slice Thickness ◽  
Radiation Treatment Planning ◽  
Content Type ◽  
Follow Up Studies ◽  
Fine Print

Object. Innovative new software solutions may enable image fusion to produce the desired data superposition for precise target definition and follow-up studies in radiosurgery/stereotactic radiotherapy in patients with intracranial lesions. The aim is to integrate the anatomical and functional information completely into the radiation treatment planning and to achieve an exact comparison for follow-up examinations. Special conditions and advantages of BrainLAB's fully automatic image fusion system are evaluated and described for this purpose. Methods. In 458 patients, the radiation treatment planning and some follow-up studies were performed using an automatic image fusion technique involving the use of different imaging modalities. Each fusion was visually checked and corrected as necessary. The computerized tomography (CT) scans for radiation treatment planning (slice thickness 1.25 mm), as well as stereotactic angiography for arteriovenous malformations, were acquired using head fixation with stereotactic arc or, in the case of stereotactic radiotherapy, with a relocatable stereotactic mask. Different magnetic resonance (MR) imaging sequences (T1, T2, and fluid-attenuated inversion-recovery images) and positron emission tomography (PET) scans were obtained without head fixation. Fusion results and the effects on radiation treatment planning and follow-up studies were analyzed. The precision level of the results of the automatic fusion depended primarily on the image quality, especially the slice thickness and the field homogeneity when using MR images, as well as on patient movement during data acquisition. Fully automated image fusion of different MR, CT, and PET studies was performed for each patient. Only in a few cases was it necessary to correct the fusion manually after visual evaluation. These corrections were minor and did not materially affect treatment planning. High-quality fusion of thin slices of a region of interest with a complete head data set could be performed easily. The target volume for radiation treatment planning could be accurately delineated using multimodal information provided by CT, MR, angiography, and PET studies. The fusion of follow-up image data sets yielded results that could be successfully compared and quantitatively evaluated. Conclusions. Depending on the quality of the originally acquired image, automated image fusion can be a very valuable tool, allowing for fast (∼ 1–2 minute) and precise fusion of all relevant data sets. Fused multimodality imaging improves the target volume definition for radiation treatment planning. High-quality follow-up image data sets should be acquired for image fusion to provide exactly comparable slices and volumetric results that will contribute to quality contol.

Mean reversion in corporate leverage: evidence from India

2019 ◽  
Vol 45 (9) ◽  
pp. 1183-1198
Author(s):  
Gaurav S. Chauhan ◽  
Pradip Banerjee
Keyword(s):  
Capital Structure ◽  
Emerging Market ◽  
Simulated Data ◽  
Mean Reversion ◽  
Developed Countries ◽  
Data Sets ◽  
Debt Ratio ◽  
Testing Strategy ◽  
Content Type ◽  
Financing Behavior

Purpose Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure. Design/methodology/approach The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form. Findings Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly. Research limitations/implications The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms. Practical implications The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate. Originality/value This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

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