2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第3页

Conservative Quantization of Fast Covariance Intersection 快速协方差相交的保守量化

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235249

Christopher Funk, B. Noack, U. Hanebeck

引用次数: 7

Machine Assisted Video Tagging of Elderly Activities in K-Log Centre K-Log中心长者活动的机器辅助视像标签

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235269

Chanwoong Lee, Hyorim Choi, Shapna Muralidharan, H. Ko, Byounghyun Yoo, G. Kim

{"title":"Machine Assisted Video Tagging of Elderly Activities in K-Log Centre","authors":"Chanwoong Lee, Hyorim Choi, Shapna Muralidharan, H. Ko, Byounghyun Yoo, G. Kim","doi":"10.1109/MFI49285.2020.9235269","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235269","url":null,"abstract":"In a rapidly aging society, like in South Korea, the number of Alzheimer’s Disease (AD) patients is a significant public health problem, and the need for specialized healthcare centers is in high demand. Healthcare providers generally rely on caregivers (CG) for elderly persons with AD to monitor and help them in their daily activities. K-Log Centre is a healthcare provider located in Korea to help AD patients meet their daily needs with assistance from CG in the center. The CG’S in the K-Log Centre need to attend the patients’ unique demands and everyday essentials for long-term care. Moreover, the CG also describes and logs the day-to-day activities in Activities of Daily Living (ADL) log, which comprises various events in detail. The CG’s logging activities can overburden their work, leading to appalling results like suffering quality of elderly care and hiring additional CG’s to maintain the quality of care and a negative feedback cycle. In this paper, we have analyzed this impending issue in K-Log Centre and propose a method to facilitate machine-assisted human tagging of videos for logging of the elderly activities using Human Activity Recognition (HAR). To enable the scenario, we use a You Only Look Once (YOLO-v3)-based deep learning method for object detection and use it for HAR creating a multi-modal machine-assisted human tagging of videos. The proposed algorithm detects the HAR with a precision of 98.4%. After designing the HAR model, we have tested it in a live video feed from the K-Log Centre to test the proposed method. The model showed an accuracy of 81.4% in live data, reducing the logging activities of the CG’s.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"14 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":"116776040","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

Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles 将ROS推向阴暗面:用于自动驾驶汽车混合现实测试系统的基于ROS的联合仿真架构

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235238

M. Zofka, Lars Töttel, Maximilian Zipfl, Marc Heinrich, Tobias Fleck, P. Schulz, Johann Marius Zöllner

{"title":"Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles","authors":"M. Zofka, Lars Töttel, Maximilian Zipfl, Marc Heinrich, Tobias Fleck, P. Schulz, Johann Marius Zöllner","doi":"10.1109/MFI49285.2020.9235238","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235238","url":null,"abstract":"Validation and verification of autonomous vehicles is still an unsolved problem. Although virtual approaches promise a cost efficient and reproducible solution, a most comprehensive and realistic representation of the real world traffic domain is required in order to make valuable statements about the performance of a highly automated driving (HAD) function. Models from different domain experts offer a repository of such representations. However, these models must be linked together for an extensive and uniform mapping of real world traffic domain for HAD performance assessment.Hereby, we propose the concept of a co-simulation architecture built upon the Robot Operating System (ROS) for both coupling and for integration of different domain expert models, immersion and stimulation of real pedestrians as well as AD systems into a common test system. This enables a unified way of generating ground truth for the performance assessment of multi-sensorial AD systems. We demonstrate the applicability of the ROS powered co-simulation by coupling behavior models in our mixed reality environment.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"60 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":"128340412","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

Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems 在通信受限的多机器人系统中提高学习率的运动模式自动发现

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235218

Taeyeong Choi, Theodore P. Pavlic

{"title":"Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems","authors":"Taeyeong Choi, Theodore P. Pavlic","doi":"10.1109/MFI49285.2020.9235218","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235218","url":null,"abstract":"Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner’s motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner’s motion, SRS simultaneously improves the learner’s predictions of all other teammates and thus can achieve similar performance as prior methods with less data.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"5 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":"125935358","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: 1

Continuous Fusion of IMU and Pose Data using Uniform B-Spline 基于均匀b样条的IMU和位姿数据连续融合

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235248

Haohao Hu, Johannes Beck, M. Lauer, C. Stiller

{"title":"Continuous Fusion of IMU and Pose Data using Uniform B-Spline","authors":"Haohao Hu, Johannes Beck, M. Lauer, C. Stiller","doi":"10.1109/MFI49285.2020.9235248","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235248","url":null,"abstract":"In this work, we present an uniform B-spline based continuous fusion approach, which fuses the motion data from an inertial measurement unit and the pose data from a visual localization system accurately, efficiently and continu-ously. Currently, in the domain of robotics and autonomous driving, most of the ego motion fusion approaches are filter based or pose graph based. By using the filter based approaches like the Kalman Filter or the Particle Filter, usually, many parameters should be set carefully, which is a big overhead. Besides that, the filter based approaches can only fuse data in a time forwards direction, which is a big disadvantage in processing async data. Since the pose graph based approaches only fuse the pose data, the inertial measurement unit data should be integrated to estimate the corresponding pose data firstly, which can however bring accumulated error into the fusion system. Additionally, the filter based approaches and the pose graph based approaches only provide discrete fusion results, which may decrease the accuracy of the data processing steps afterwards. Since the fusion approach is generally needed for robots and automated driving vehicles, it is a major goal to make it more accurate, robust, efficient and continuous. Therefore, in this work, we address this problem and apply the axis-angle rotation representation method, the Rodrigues’ formula and the uniform B-spline implementation to solve the ego motion fusion problem continuously. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results.","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":"129840726","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

Sparse Magnetometer-Free Real-Time Inertial Hand Motion Tracking 稀疏无磁强计实时惯性手部运动跟踪

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235262

Aaron Grapentin, Dustin Lehmann, Ardjola Zhupa, T. Seel

{"title":"Sparse Magnetometer-Free Real-Time Inertial Hand Motion Tracking","authors":"Aaron Grapentin, Dustin Lehmann, Ardjola Zhupa, T. Seel","doi":"10.1109/MFI49285.2020.9235262","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235262","url":null,"abstract":"Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.","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":"132705754","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

Nonlinear von Mises–Fisher Filtering Based on Isotropic Deterministic Sampling 基于各向同性确定性采样的非线性von Mises-Fisher滤波

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235260

Kailai Li, F. Pfaff, U. Hanebeck

引用次数: 6

Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility 基于直接点云可视性的UGV和无人机在线三维边界探索

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235268

Jason L. Williams, Shu Jiang, M. O'Brien, Glenn Wagner, E. Hernández, Mark Cox, Alex Pitt, R. Arkin, N. Hudson

{"title":"Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility","authors":"Jason L. Williams, Shu Jiang, M. O'Brien, Glenn Wagner, E. Hernández, Mark Cox, Alex Pitt, R. Arkin, N. Hudson","doi":"10.1109/MFI49285.2020.9235268","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235268","url":null,"abstract":"While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"492 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":"116030226","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: 20

SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation SemanticVoxels:使用LiDAR点云和语义分割的3D行人检测的顺序融合

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235240

Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, C. Stiller

{"title":"SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation","authors":"Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, C. Stiller","doi":"10.1109/MFI49285.2020.9235240","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235240","url":null,"abstract":"3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In this paper, we propose a generalization of PointPainting to be able to apply fusion at different levels. After the semantic augmentation of the point cloud, we encode raw point data in pillars to get geometric features and semantic point data in voxels to get semantic features and fuse them in an effective way. Experimental results on the KITTI test set show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird’s eye view pedestrian detection benchmarks. In particular, our approach demonstrates its strength in detecting challenging pedestrian cases and outperforms current state-of-the-art approaches.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"99 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":"132944957","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: 20

AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping AirMuseum:用于立体视觉和惯性同步定位和映射的异构多机器人数据集

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235257

Rodolphe Dubois, A. Eudes, V. Fremont

引用次数: 5