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

A Continuous Probabilistic Origin Association Filter for Extended Object Tracking 一种用于扩展目标跟踪的连续概率起源关联滤波器

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235214

Philipp Berthold, Martin Michaelis, T. Luettel, D. Meissner, H. Wuensche

{"title":"A Continuous Probabilistic Origin Association Filter for Extended Object Tracking","authors":"Philipp Berthold, Martin Michaelis, T. Luettel, D. Meissner, H. Wuensche","doi":"10.1109/MFI49285.2020.9235214","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235214","url":null,"abstract":"One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"37 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":"114999843","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: 0

Evaluation of optical motion capture system performance in humanrobot collaborative cells 人机协作单元光学动作捕捉系统性能评价

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235242

Leticia González, J. C. Álvarez, Antonio M. López, D. Álvarez

引用次数: 0

Local and Global Sensors for Collision Avoidance 避碰的局部和全局传感器

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235223

Aquib Rashid, Kannan Peesapati, M. Bdiwi, Sebastian Krusche, W. Hardt, M. Putz

引用次数: 5

The Interacting Multiple Model Filter on Boxplus-Manifolds 箱加流形上的交互多模型滤波器

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235232

Tom L. Koller, U. Frese

引用次数: 1

Field Experiments on Shooter State Estimation Accuracy Based on Incomplete Acoustic Measurements 基于不完全声学测量的射击状态估计精度的现场实验

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235221

Luisa Still, M. Oispuu

引用次数: 1

Efficient Deterministic Conditional Sampling of Multivariate Gaussian Densities 多元高斯密度的高效确定性条件抽样

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235212

Daniel Frisch, U. Hanebeck

{"title":"Efficient Deterministic Conditional Sampling of Multivariate Gaussian Densities","authors":"Daniel Frisch, U. Hanebeck","doi":"10.1109/MFI49285.2020.9235212","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235212","url":null,"abstract":"We propose a fast method for deterministic multi-variate Gaussian sampling. In many application scenarios, the commonly used stochastic Gaussian sampling could simply be replaced by our method – yielding comparable results with a much smaller number of samples. Conformity between the reference Gaussian density function and the distribution of samples is established by minimizing a distance measure between Gaussian density and Dirac mixture density. A modified Cramér-von Mises distance of the Localized Cumulative Distributions (LCDs) of the two densities is employed that allows a direct comparison between continuous and discrete densities in higher dimensions. Because numerical minimization of this distance measure is not feasible under real time constraints, we propose to build a library that maintains sample locations from the standard normal distribution as a template for each number of samples in each dimension. During run time, the requested sample set is re-scaled according to the eigenvalues of the covariance matrix, rotated according to the eigenvectors, and translated according to the mean vector, thus adequately representing arbitrary multivariate normal distributions.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"2 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":"129573578","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

Observability driven Multi-modal Line-scan Camera Calibration 可观测性驱动的多模态线扫描相机校准

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235226

Jasprabhjit Mehami, Teresa Vidal-Calleja, A. Alempijevic

{"title":"Observability driven Multi-modal Line-scan Camera Calibration","authors":"Jasprabhjit Mehami, Teresa Vidal-Calleja, A. Alempijevic","doi":"10.1109/MFI49285.2020.9235226","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235226","url":null,"abstract":"Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"38 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":"114708453","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: 0

Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models 基于学习空间分布模型的汽车雷达贝叶斯扩展目标跟踪

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235255

J. Honer, Hauke Kaulbersch

引用次数: 3

A Unified Approach to The Orbital Tracking Problem 轨道跟踪问题的统一方法

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235258

J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein

引用次数: 2

Effect of Kernel Function to Magnetic Map and Evaluation of Localization of Magnetic Navigation 核函数对磁图的影响及磁导航定位评价

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235259

Takumi Takebayashi, Renato Miyagusuku, K. Ozaki

引用次数: 1