2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第4页

Identification of kinematic vehicle model parameters for localization purposes 以定位为目的的车辆运动学模型参数识别

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235246

Máté Fazekas, P. Gáspár, B. Németh

{"title":"Identification of kinematic vehicle model parameters for localization purposes","authors":"Máté Fazekas, P. Gáspár, B. Németh","doi":"10.1109/MFI49285.2020.9235246","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235246","url":null,"abstract":"The article proposes a parameter identification algorithm for a kinematic vehicle model from real measurements of on-board sensors. The motivation of the paper is to improve the localization in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the poor feature number, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations the wheel encoder-based odometry can be an appropriate choice for pose estimation, however, this method suffers from parameter uncertainty. The proposed method combines the Gauss-Newton non-linear estimation techniques with Kalman-filtering in an iterative loop and identifies the wheel circumferences and track width parameters in three steps. The estimation architecture eliminates the convergence to a local optimum and the divergence resulted in the highly uncertain initial parameter values. The identification performance is verified by a real test of a compact car. The results are compared with the nominal setting, which should be applied in the lack of identification.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125644267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Bayesian Deghosting Algorithm for Multiple Target Tracking 多目标跟踪的贝叶斯去重影算法

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235215

P. Kulmon

引用次数: 3

Assymetric Noise Tailoring for Vehicle Lidar data in Extended Object Tracking 扩展目标跟踪中车载激光雷达数据的非对称噪声裁剪

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235253

Hauke Kaulbersch, J. Honer, M. Baum

引用次数: 0

Localization and velocity estimation based on multiple bistatic measurements 基于多个双基地测量的定位和速度估计

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235256

Sebastian Woischneck, D. Fränken

引用次数: 2

Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking 递归神经网络在多目标跟踪中的不确定性估计

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235216

Daniel Pollithy, Marcel Reith-Braun, F. Pfaff, U. Hanebeck

引用次数: 4

Probabilistic Programming Languages for Modeling Autonomous Systems 自治系统建模的概率编程语言

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235230

Seyed Mahdi Shamsi, Gian Pietro Farina, Marco Gaboardi, N. Napp

{"title":"Probabilistic Programming Languages for Modeling Autonomous Systems","authors":"Seyed Mahdi Shamsi, Gian Pietro Farina, Marco Gaboardi, N. Napp","doi":"10.1109/MFI49285.2020.9235230","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235230","url":null,"abstract":"We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Estimating Correlated Angles Using the Hypertoroidal Grid Filter 利用超环面网格滤波器估计相关角度

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235220

F. Pfaff, Kailai Li, U. Hanebeck

引用次数: 4

Combination of Maximum Correntropy Criterion & α-Rényi Divergence for a Robust and Fail-Safe Multi-Sensor Data Fusion 基于最大熵准则和α- rsamnyi散度的鲁棒故障安全多传感器数据融合

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235244

Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar, N. Moubayed

{"title":"Combination of Maximum Correntropy Criterion & α-Rényi Divergence for a Robust and Fail-Safe Multi-Sensor Data Fusion","authors":"Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar, N. Moubayed","doi":"10.1109/MFI49285.2020.9235244","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235244","url":null,"abstract":"A combination of a robust optimality criterion, the Maximum Correntropy Criterion (MCC), and a powerful Fault Detection and Exclusion (FDE) strategy for a robust and fault-tolerant multi-sensor fusion approach is presented in this paper taking advantage of the information theory. The used estimator is called the MCCNIF, which is in the Nonlinear Information Filter (NIF) under the MCC. The NIF deals well with Gaussian noises but, its performance decreases when abruptly facing heavy non-Gaussian noises causing a divergence. Conversely, the NIF deals fairly with nonlinearity problems. Hence, to deal with non-Gaussian noises, the MCC shows good performance especially with shot noises and Gaussian mixture noises. To detect and exclude the erroneous measurements, an FDE layer, based on α-Rényi Divergence (α-RD) between the a priori and a posteriori probability distributions, is created. Then an adaptive threshold is calculated as a decision support based on the α-Rényi criterion (α-Rc).In order to test in real conditions the proposed framework, an autonomous vehicle multi-sensor localization example is taken. Indeed, for this application, in stringent environments (such as urban canyon, building, forests…), it is necessary to ensure both integrity and accuracy. The proposed solution is to combine the Global Navigation Satellite System (GNSS) data with the odometer (odo) data by a tight integration. The main contributions of this paper are the design and development of unique framework integrating a robust filter the MCCNIF and an FDE method using residual based on α-RD with an adaptive threshold. Real experimental data are presented and encourages the validation of the proposed approach.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors 基于手势识别和接近传感器的直观人机交互

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235264

Gorkem Anil Al, P. Estrela, Uriel Martinez-Hernandez

{"title":"Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors","authors":"Gorkem Anil Al, P. Estrela, Uriel Martinez-Hernandez","doi":"10.1109/MFI49285.2020.9235264","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235264","url":null,"abstract":"In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116364987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

FAA-NASA vs. Lane-Based Strategic Deconfliction FAA-NASA vs.基于航线的战略冲突

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235239

D. Sacharny, T. Henderson, Michael Cline, Ben Russon, EJay Guo

{"title":"FAA-NASA vs. Lane-Based Strategic Deconfliction","authors":"D. Sacharny, T. Henderson, Michael Cline, Ben Russon, EJay Guo","doi":"10.1109/MFI49285.2020.9235239","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235239","url":null,"abstract":"The Federal Aviation Administration (FAA) and NASA have provided guidelines for Unmanned Aircraft Systems (UAS) to ensure adequate safety separation of aircraft, and in terms of UAS Traffic Management (UTM) have stated[1]:A UTM Operation should be free of 4-D intersection with all other known UTM Operations prior to departure and this should be known as Strategic Deconfliction within UTM … A UTM Operator must have a facility to negotiate deconfliction of operations with other UTM Operators … There needs to be a capability to allow for intersecting operations.The latter statement means that UTM Operators must be able to fly safely in the same geographic area. The current FAA-NASA approach to strategic deconfliction is to provide a set of geographic grid elements, and then have every new flight pairwise deconflict with UTM Operators with flights in the same grid elements. Note that this imposes a high computational burden in resolving these 4D flight paths, and has side effects in terms of limiting access to the airspace (e.g., if a new flight is deconflicted and added to the common grid elements during this analysis, then the new flight must start all over).We have proposed a lane-based approach to large-scale UAS traffic management [2], [3] which uses one-way lanes, and roundabouts at lane intersections to allow a much more efficient analysis and guarantee of separation safety. We present here the results of an in-depth comparison of FAA-NASA strategic deconfliction (FNSD) and Lane-based strategic deconfliction (LSD) and demonstrate that FNSD suffers from several types of complexity which are generally absent from the lane-based method. This algorithm is based on optimization methods which form the core origins of artificial intelligence.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116495914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6