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Multimodal sensor fusion with Differentiable Filters

Multimodal Sensor Fusion with Differentiable Filters

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2020 IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Michelle A. Lee Brent Yi Roberto Martín-Martín Silvio Savarese Jeannette Bohg Stanford Artificial Intelligence Lab (SAIL) Stanford University

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities according to their uncertainties. Prior work has studied how to optimally fuse different sensor modalities with analytical state estimation algorithms. However, deriving the dynamics and measurement models along with their noise profile can be difficult or lead to intractable models. Differentiable filters provide a way to learn these models end-to-end while retaining the algorithmic structure of recursive filters. This can be especially helpful when working with sensor modalities that are high dimensional and have very different characteristics. In contact-rich manipulation, we want to combine visual sensing (which gives us global information) with tactile sensing (which gives us local information). In this paper, we study new differentiable filtering architectures to fuse heterogeneous sensor information. As case studies, we evaluate three tasks: two in planar pushing (simulated and real) and one in manipulating a kinematically constrained door (simulated). In extensive evaluations, we find that differentiable filters that leverage crossmodal sensor information reach comparable accuracies to unstructured LSTM models, while presenting interpretability benefits that may be important for safety-critical systems. We also release an open-source library for creating and training differentiable Bayesian filters in PyTorch, which can be found on our project website: https://***/view/multimodalfilter.

关键词： Visualization Heuristic algorithms Filtering algorithms Information filters Robot sensing systems State estimation Task analysis

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Miniaturized low-power wearable system for human motion tacking incorporating monocular camera and inertial sensor data fusion for health applications 13

Miniaturized low-power wearable system for human motion tack...

引用

13th Smart Systems Integration conference, SSI 2019

作者： Wilk, Mariusz P. O'Flynn, Brendan Tyndall National Institute UCC Cork Ireland

ISBN: (纸本)9783800748785

Human motion tracking systems play an important role in many application spaces where the 3D pose of the body, or its parts, need to be tracked over time, e.g. fitness, rehabilitation, etc. While it is considered a solved problem in expensive infrastructure-intensive setups, it remains an active research area in low-cost and low-power applications. In this work, we propose our wearable motion tracking system that leverages the advances in the state-of-the-art in system miniaturization and integration. Our proposed system integrates a monocular camera and an inertial motion sensor (IMU) in a single wearable unit. Moreover, it can perform a six degree-of-freedom (DOF) pose detection and tracking with our novel data fusion algorithms and a smart coupling with the ambient environment. © VDE VERLAG GMBH

关键词： Degrees of freedom (mechanics)

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Automotive Radar Dataset for Deep Learning Based 3D Object Detection 16

Automotive Radar Dataset for Deep Learning Based 3D Object D...

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16th European Radar conference (EuRAD) / European Microwave Week

作者： Meyer, Michael Kuschk, Georg Astyx GmbH Ottobrunn Germany

ISBN: (纸本)9782874870576

We present a radar-centric automotive dataset based on radar, lidar and camera data for the purpose of 3D object detection. Our main focus is to provide high resolution radar data to the research community, facilitating and stimulating research on algorithms using radar sensor data. To this end, semi-automatically generated and manually refined 3D ground truth data for object detection is provided. We describe the complete process of generating such a dataset, highlight some main features of the corresponding high-resolution radar and demonstrate its usage for level 3-5 autonomous driving applications by showing results of a deep learning based 3D object detection algorithm on this dataset. Our dataset will be available online at: ***

关键词： 3D object detection deep learning dataset multi sensor fusion radar

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architectures for Radiofrequency and Optronics sensors onboard Remotely Piloted Aerial Systems (RPAS) 16

Architectures for Radiofrequency and Optronics sensors onboa...

引用

conference on Electro-Optical and Infrared Systems - Technology and applications XVI

作者： Jimenez, Adrian Alfageme, Miguel Llamazares, David Polo, Valentin Simon, Santiago Perez Garcia, Elisabeth Everis Aerosp & Def Madrid Spain DAS Photon Valencia Spain

ISBN: (数字)9781510630222

ISBN: (纸本)9781510630222

This work has been developed under the EDA contract no ***.137 within the Electro Optical sensors technologies Captech in EDA. In the Defence domain, the different assets are increasingly required to operate in a multi-role, multi-purpose manner within a wide range of possible missions, locations and operational environments. The sensors systems, both Radiofrequency (RF) and Electro-optic (EO), are in continuous development, and the next generation will be developed with multifunctional capabilities and increased performance, therefore new architectures should be define to handle this and to combine the information offered by them in the most effective way. This will provide the capability of operation in all weather, all time, difficult conditions with a broad range of threats immersed in strong clutter and electromagnetic interference (EMI) environments and the possibility to quickly adapt to each mission scenarios. The main goal is the definition of an Interoperable, Modular, Open and Scalable architecture (IMOSA) to achieve interoperability within payloads -mainly EO and RF sensors- for Class I Remotely Piloted Aircraft Systems (RPAS) which can also be applied to Class II RPAS. The implementation of this architecture allows that a single RPAS will be able to carry a variety of sensors on-board, regardless of its manufacturer, including the maximization of sensor data fusion performance, enhancing the RPAS capabilities in hostile environments and the improvement in the payload sensors interoperability and integration properties together with a higher reliability, flexibility and a lower product life-cycle costs for both, manufacturers and final users. This study includes a review of the state of the art of the related technologies, the definition of scenarios, requirements and business cases to justify its implementation, the definition of an architecture and BB initially designed and the generation of a roadmap to implement this concept successfully in the

关键词： IMOSA sensor EO RF RPAS architectures interoperability

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Performance Evaluation of MEMS Pressure sensors

Performance Evaluation of MEMS Pressure Sensors

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National conference with International Participation (ELECTRONICA)

作者： Borislav Ganev Dimitar Nikolov Marin B. Marinov Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9781728175317

ISBN: (纸本)9781728175324

sensors for atmospheric pressure monitoring are important for many applications. They include indoor positioning, environmental monitoring, and more. This paper presents a study of the main characteristics of micro mechanical atmospheric sensors. Four different models of microelectromechanical (MEMS) pressure sensors were studied. Some of the models along with the pressure measure additional parameters such as temperature, relative humidity, and air quality. The research evaluated the accuracy of the sensors, the impact of the change of basic atmospheric parameters on it, and their noise characteristics. The results show that low-budget MEMS pressure sensors offer many advantages for the efficient implementation of different applications. An important prerequisite for this is the use of appropriate algorithms for the fusion of sensor data, which can increase the reliability of their data.

关键词： sensors sensor phenomena and characterization Temperature sensors Pressure sensors Temperature measurement Micromechanical devices Pressure measurement

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LiDAR-camera fusion for 3D object detection

LiDAR-camera fusion for 3D object detection

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2020 Autonomous Vehicles and Machines conference, AVM 2020

作者： Bhanushali, Darshan Relyea, Robert Manghi, Karan Vashist, Abhishek Hochgraf, Clark Ganguly, Amlan Kwasinski, Andres Kuhl, Michael E. Ptucha, Raymond Rochester Institute of Technology RochesterNY United States

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset. © 2020 Society for Imaging Science and Technology. All rights reserved.

关键词： Object detection

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Comparative Study on Energy Efficiency of WSNs and WMSNs for Surveillance applications 26

Comparative Study on Energy Efficiency of WSNs and WMSNs for...

引用

26th International conference on Telecommunications (ICT)

作者： Kizilkaya, Burak Ever, Enver Yazici, Adnan Middle East Tech Univ Comp Engn Northern Cyprus CampusMersin 10 Nicosia Turkey Nazarbayev Univ Dept Comp Sci Astana Kazakhstan

ISBN: (纸本)9781728102733

The infrastructure required to deliver various "Internet of Things" (IoT) services is expected to be widely available in the very near future for application areas such as Smart Cities, Smart Homes, Intelligent Transportation and Smart Monitoring in the coming decades. In fact, it is expected that the number of devices connected to the Internet will exceed 50 billion by 2020. It is possible to use these infrastructures for environmental monitoring systems, especially for the early detection and prevention of disasters such as forest fires. Wireless sensors are an essential part of the IoT infrastructure and environmental monitoring/surveillance systems. Wireless sensor networks based on scalar sensors and wireless multimedia sensor networks are widely used for environmental monitoring. Multimedia surveillance systems have better accuracy but a shorter lifetime with multimedia surveillance systems. Therefore, it is necessary to study in detail their energy efficiency to better understand the introduction of more efficient algorithms and architectures. In this study, the lifetimes of the Wireless sensor Networks(WSNs) and Wireless Multimedia sensor Networks(WMSNs) are considered in a comparative way. The results are presented for a forest fire detection case study using simulations as well as an example of a test bench to confirm the accuracy of the simulation tool used.

关键词： Environment monitoring WSNs WMSNs simulation test-bed

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A Graphical Language for Flexible Inference in Robotics and Vision (Invited Talk) 2019

A Graphical Language for Flexible Inference in Robotics and ...

引用

International conference on Systems, Programming, Languages, and applications: Software for Humanity (SPLASH)

作者： Dellaert, Frank Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450369923

In robotics and computer vision, there are many inference problems that come down to fusing the information from many different sensors, or a small number of sensors deployed over time. Dellaert will review how these and other problems can be posed in terms of factor graphs, which provide a graphical language in which to develop and collaborate on such problems. The talk will emphasize the advantages and intuition that come with these graphical languages, leading to both offline and online sensor fusion algorithms. One of Frank Dellaert’s current research directions is exploring the use of code generation to create high-performance, certifiable code to support autonomous systems in the real world.

关键词： Robotics Computer Vision Probabilistic Inference sensor fusion

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A Smart Self-Organizing Node Deployment Algorithm in Wireless sensor Networks 15

A Smart Self-Organizing Node Deployment Algorithm in Wireles...

引用

15th IEEE/IFIP Wireless On-Demand Network Systems and Services conference (WONS)

作者： Ghahroudi, Mahsa Sadeghi Shahrabi, Alireza Boutaleb, Tuleen Glasgow Caledonian Univ Sch Comp Engn & Built Environm Glasgow Lanark Scotland

ISBN: (纸本)9783903176133

Node deployment is one of the fundamental issues in Wireless sensor Networks (WSNs) which has not only a direct impact on the effectiveness of other operations, such as routing and data fusion, but also on the appropriateness of the provided coverage expected in many applications such as national security, surveillance, military, health care, and environmental monitoring. In mobile sensor networks, the resource-constrained move-assisted sensor nodes are used in an area to maximise the coverage within a reasonable time and energy cost. Recently, a family of algorithms, inspired by the equilibrium of molecules, have been proposed to address the coverage issue. However, these solutions lead to high energy cost and latency due to two major issues. First, almost all the nodes in the network try to move to a new position at each stage. Even worse, the decision made at each node on to which point to move at each stage is purely based on obsolete information, i.e. the current locations of moving neighbouring nodes. In this paper, we propose a new distributed algorithm, called SSND, to efficiently provide the maximum coverage for WSNs that use mobile nodes. SSND avoids to collectively, and blindly, move sensor nodes at each step but to apply an eligibility function to elect a few nodes to move using the valid information to obtain the maximum effect. Our extensive simulation study under various operational conditions shows not only a higher percentage of area coverage by SSND but with much lower power consumption and latency than those of the other protocols recently reported in the literature.

关键词： distributed wireless sensor network energy efficiency smart node deployment algorithms Self-Organizing

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An Efficient Machine Learning Algorithm For Spatial Tracking Of Correlated Signals In Wireless sensor Field

An Efficient Machine Learning Algorithm For Spatial Tracking...

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Australasian Telecommunication Networks and applications conference, ATNAC

作者： Hadi Alasti School of Polytechnic Purdue University Fort Wayne (PFW) Fort Wayne IN USA

ISBN: (数字)9781728188270

ISBN: (纸本)9781728188287

An efficient machine learning algorithm based on stochastic gradient is proposed and discussed for spatial tracking of correlated spatial signals from the sensor observations in wireless sensor field. The proposed algorithm can be used for environmental monitoring applications such as efficient temporal monitoring of temperature in hot island, or efficient monitoring of the distribution of pollutant gasses in wide areas for example terrain of large cities, etc. The proposed algorithm is computationally efficient and is low cost. In this paper the number of reporting sensors in tracking of signal is defined as cost. The spatial signal is compressed into a number of its isocontours at specific levels and the sensors whose sensor readings are in given margin of these contour levels, report their sensor readings to the fusion center (FC). The algorithm is done in two phases of spatial modeling and spatial tracking, where it uses the correlation between the spatial signal before and after variation of the spatial signal and updates the new set of contour levels for spatial tracking. The proposed machine learning algorithm finds the modeling parameters during the spatial modeling phase, and then updates them in spatial tracking phase. The performance analysis of the proposed algorithm shows that it successfully tracks the spatial variations of the signal at low cost with similar modeling performance to the spatial modeling.

关键词： Machine learning algorithms Stochastic processes Mathematical model Convergence Wireless sensor networks Wireless communication Monitoring

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