scholarly journals Monocular Visual Inertial Direct SLAM with Robust Scale Estimation for Ground Robots/Vehicles

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Bismaya Sahoo ◽  
Mohammad Biglarbegian ◽  
William Melek
Keyword(s):  
Pose Prediction ◽  
Geometric Information ◽  
Land Vehicles ◽  
Tightly Coupled ◽  
Novel Method ◽  
Epipolar Constraints ◽  
Inertial Measurements ◽  
Primary Contribution ◽  
Alignment Step ◽  
Indoor And Outdoor

In this paper, we present a novel method for visual-inertial odometry for land vehicles. Our technique is robust to unintended, but unavoidable bumps, encountered when an off-road land vehicle traverses over potholes, speed-bumps or general change in terrain. In contrast to tightly-coupled methods for visual-inertial odometry, we split the joint visual and inertial residuals into two separate steps and perform the inertial optimization after the direct-visual alignment step. We utilize all visual and geometric information encoded in a keyframe by including the inverse-depth variances in our optimization objective, making our method a direct approach. The primary contribution of our work is the use of epipolar constraints, computed from a direct-image alignment, to correct pose prediction obtained by integrating IMU measurements, while simultaneously building a semi-dense map of the environment in real-time. Through experiments, both indoor and outdoor, we show that our method is robust to sudden spikes in inertial measurements while achieving better accuracy than the state-of-the art direct, tightly-coupled visual-inertial fusion method.

scholarly journals A Tightly Coupled Positioning Solution for Land Vehicles in Urban Canyons

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Li ◽  
Rong Jiang ◽  
Xianghui Song ◽  
Bin Li
Keyword(s):  
Urban Areas ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Information Source ◽  
Satellite Measurement ◽  
3D Navigation ◽  
Filter Model ◽  
Land Vehicles ◽  
Tightly Coupled

The integration between Global Navigation Satellite System (GNSS) and on-board sensors is widely used for vehicle positioning. However, as the main information source in the integration, the positioning performance of single- or multiconstellation GNSSs is severely degraded in urban canyons due to the effects of Non-Line-Of-Sight (NLOS) and multipath propagations. How to mitigate such effects is vital to achieve accurate positioning performance in urban canyons. This paper proposes a tightly coupled positioning solution for land vehicles, fusing dual-constellation GNSSs with other low-cost complementary sensors. First, the nonlinear filter model is established based on a cost-effective reduced inertial sensor system with 3D navigation solution. Then, an adaptive fuzzy unscented Kalman filter (AF-UKF) algorithm is developed to achieve the global fusion. In the implementation of AF-UKF, the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received satellite measurement to effectively mitigate the NLOS and multipath interferences in urban areas. Finally, the proposed solution is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed solution.

scholarly journals INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES

2016 ◽  
Vol XLI-B6 ◽  
pp. 251-258 ◽  
Author(s):  
Tsung-Che Huang ◽  
Yi-Hsing Tseng
Keyword(s):  
Indoor Positioning ◽  
Image Features ◽  
Bundle Adjustment ◽  
Target Space ◽  
Mobile Mapping ◽  
Technology Roadmap ◽  
Mapping Technology ◽  
Panoramic Images ◽  
Novel Method ◽  
Indoor And Outdoor

Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.

scholarly journals A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

2021 ◽  
Vol 14 (1) ◽  
pp. 27
Author(s):  
Changqiang Wang ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yushi Hao ◽  
Zhengxu Shi ◽  
...  
Keyword(s):  
High Precision ◽  
Navigation System ◽  
Autonomous Vehicles ◽  
Outdoor Environment ◽  
Precision Positioning ◽  
Outdoor Environments ◽  
Tightly Coupled ◽  
Seamless Positioning ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

scholarly journals VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4386
Author(s):  
Jingzhe Wang ◽  
Leilei Li ◽  
Huan Yu ◽  
Xunya Gui ◽  
Zucheng Li
Keyword(s):  
Large Scale ◽  
Linear Optimization ◽  
Position Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Non Linear ◽  
Outdoor Environments ◽  
Novel Method ◽  
And Performance ◽  
Inertial Measurements

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system’s viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

Blower-door estimates of PM2.5 deposition rates and penetration factors in an idealized room

2020 ◽  
pp. 1420326X2094442 ◽  
Author(s):  
Yonghang Lai ◽  
Ian Ridley ◽  
Peter Brimblecombe
Keyword(s):  
Deposition Rate ◽  
Coefficient Of Variation ◽  
Particle Deposition ◽  
Experimental Methods ◽  
Test Cell ◽  
Deposition Rates ◽  
Penetration Factor ◽  
Surface Materials ◽  
Novel Method ◽  
Indoor And Outdoor

Particle deposition and penetration in buildings has been widely studied, but the effect of indoor characteristics merits further investigation, so improved experimental methods may be needed. The present study measured indoor and outdoor concentrations of PM2.5 and estimated PM2.5 deposition rates and penetration factors under a variety of different indoor situations, with a novel method (blower-door method). The blower-door method is compared with the standard decay and rebound method for an idealized room (a portable building test cell; 6.08 m [Formula: see text] 2.40 m [Formula: see text] 2.60 m) under eight testing scenarios (empty, cardboard boxes in three arrangements, terry cloth wall covering, and three sets of window holes); run three times to establish the coefficient of variation representing precision. Results show that higher induced indoor–outdoor pressure differences cause a larger variation of estimated effective deposition rate on different indoor surfaces. The deposition rate and penetration factor may be influenced by indoor surface materials. The blower-door method gives higher precision for the estimates, and detects subtle differences in penetration factors, which may be difficult using the decay and rebound method.

scholarly journals A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3418 ◽  
Author(s):  
Junxiang Jiang ◽  
Xiaoji Niu ◽  
Ruonan Guo ◽  
Jingnan Liu
Keyword(s):  
Navigation System ◽  
Motion Tracking ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Inertial Navigation ◽  
Sliding Window ◽  
Tightly Coupled ◽  
Inertial Measurements ◽  
Space Requirements ◽  
Sensing Characteristics

The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms.

Surface Patch Primitive Based Object Modeling from CAD Data

2012 ◽  
Vol 162 ◽  
pp. 179-183 ◽  
Author(s):  
Sukhan Lee ◽  
Kyeong Dae Yoo ◽  
Jae Woong Kim ◽  
Moon Ju Lee
Keyword(s):  
Automated Reasoning ◽  
Direct Application ◽  
Robotic Manipulation ◽  
Surface Patch ◽  
Geometric Information ◽  
Grasp Planning ◽  
Manufacturing Automation ◽  
Novel Method ◽  
Robotic Automation ◽  
Geometric Primitives

For manufacturing automation, for instance, the robotic automation of automobile sub-assembly, CAD data serves as DB offering the geometric information of parts essential for robotic manipulation. However, a direct application of CAD for the robotic manipulation of parts may be of an issue, due to the fact that the particular format of the CAD data available, e.g., STL, does not directly provide certain geometric entities such as surface patch primitives and/or features that are required for robotic manipulation. In this paper, we present a novel method for extracting geometric primitives and/or features, such surface patch primitives as planar, cylindrical, conic, and spherical patches, from the STL format of CAD data, such that an industrial part/object can be represented as a logical sum of these surface patch primitives extracted. This surface patch primitive based modeling makes the automated reasoning involved in the recognition and pose estimation, as well as the grasp planning, of parts/objects easy to be done. The proposed method is applied to various CAD data samples for experimentation: the results demonstrate the reliability as well as the computational efficiency of the proposed method in the extraction of surface patch primitives.

scholarly journals PaSiT: a novel approach based on short-oligonucleotide frequencies for efficient bacterial identification and typing

2020 ◽  
Vol 36 (8) ◽  
pp. 2337-2344 ◽  
Author(s):  
Gleb Goussarov ◽  
Ilse Cleenwerck ◽  
Mohamed Mysara ◽  
Natalie Leys ◽  
Pieter Monsieurs ◽  
...  
Keyword(s):  
Large Scale ◽  
Bacterial Species ◽  
Supplementary Information ◽  
Nucleotide Identity ◽  
Average Nucleotide Identity ◽  
Bacterial Genomes ◽  
Short Oligonucleotide ◽  
Novel Approach ◽  
Novel Method ◽  
Alignment Step

Abstract Motivation One of the most widespread methods used in taxonomy studies to distinguish between strains or taxa is the calculation of average nucleotide identity. It requires a computationally expensive alignment step and is therefore not suitable for large-scale comparisons. Short oligonucleotide-based methods do offer a faster alternative but at the expense of accuracy. Here, we aim to address this shortcoming by providing a software that implements a novel method based on short-oligonucleotide frequencies to compute inter-genomic distances. Results Our tetranucleotide and hexanucleotide implementations, which were optimized based on a taxonomically well-defined set of over 200 newly sequenced bacterial genomes, are as accurate as the short oligonucleotide-based method TETRA and average nucleotide identity, for identifying bacterial species and strains, respectively. Moreover, the lightweight nature of this method makes it applicable for large-scale analyses. Availability and implementation The method introduced here was implemented, together with other existing methods, in a dependency-free software written in C, GenDisCal, available as source code from https://github.com/LM-UGent/GenDisCal. The software supports multithreading and has been tested on Windows and Linux (CentOS). In addition, a Java-based graphical user interface that acts as a wrapper for the software is also available. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document