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Downside Hemisphere Object Detection and Localization of MAV by Fisheye Camera

Downside Hemisphere Object Detection and Localization of MAV...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Jun Zhu Jiangcheng Zhu Xudong Wan Chao Xu State Key Lab of Industrial Control Technology Zhejiang University China

For a multirotor micro aerial vehicle (MAV) flying in the outdoor environment, its downside hemisphere has richest visual information. All that information can be obtained by a single fisheye camera with larger than 180 degrees field of view (FOV). Traditionally, the unrestored fisheye image is restored to a flat image before subsequent processing, which is both resource and time consuming. In this paper, to save resource and time, a method of fisheye object detection and localization on the unrestored fisheye image is proposed. A single-stage neural network is built for object detection. To improve the performance of detector, its submodules are designed specifically by combining the central rotational property and severe distortion of the fisheye image. To meet the real-time requirements of onboard computation, the detector is also tuned to be light-weight. After that, the detected objects are localized with assistance of by a data fusion on the fisheye model and MAV sensory data (altitude, attitude, etc.), the experimental results have validated the effectiveness of the proposed methods in this paper.

关键词： Cameras Object detection Detectors Convolution Training Mathematical model Nonlinear optics

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Detection and Compensation of Motion Error for Nanomanipulation Platform in Scanning Electron Microscope

Detection and Compensation of Motion Error for Nanomanipulat...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Mingyu Wang Yaqiong Wang Zhan Yang Tao Chen Lining Sun Toshio Fukuda Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Soochow University Suzhou China Department of Micro-Nano Systems Engineering Nagoya University Nagoya Japan

Nanomanipulation system based on scanning electron microscope(SEM) with good real-time visual feedback and nanoscale observation resolution had high operability in a vacuum working environment. Different nanomanipulation tasks of carbon nanotubes (CNTs) are carried out through the nanomanipulation system in SEM. Nanomanipulation platform existed inherent manufacture errors, installation errors and other errors, and imprecise nanomanipulation system were also time-consuming and laborious for operators. this paper presentes a method of combining the visual feedback and feedforward control to detect and compensate the motion error of the multi-dimensional SmarAct nanomanipulation platform in the nanomanipulation system in SEM. this method reduces the motion error in the X-Y direction and achieved higher operating accuracy. At the different step speed, the motion error in the X direction and Y direction is 135.7nm and 112.9nm respectively. After the feedforward compensation, the motion error in the X direction and Y direction reduces to 61.3nm and 54.1nm respectively.

关键词： Nanobioscience Trajectory Ceramics Visualization Probes Scanning electron microscopy Feedforward systems

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Detection and Following of Moving Targets by an Indoor Mobile Robot using Microsoft Kinect and 2D Lidar Data

Detection and Following of Moving Targets by an Indoor Mobil...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Vasil L. Popov Sevil A. Ahmed Nikola G. Shakev Andon V. Topalov Control Systems Department Technical University of Sofia Plovdiv Bulgaria

the mobile robot following behavior is frequently considered as an important pre-requisite while developing autonomous service robots intended to co-exist with humans in a shared environment. It can also simplify the development of autonomous navigation and obstacle avoidance behavior as well as the robot ability to operate within multi-agent formations. In this investigation, an approach is proposed that allows a KUKA youBot omnidirectional mobile platform to detect and follow people carrying different objects, such as suitcases, in a shared indoor environment. Using a Kinect sensor, the mobile robot can recognize standing human with a suitcase and will begin to consider him as a potential dynamic target. the 2D lidar of the mobile platform can further detect when the above target starts to move and will begin to track it. Meanwhile, the robot will start following the human by controlling its own velocity while maintaining a pre-specified orientation and distance.

关键词： Robot sensing systems Target tracking Two dimensional displays Mobile robots Laser radar

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An Innovative robotics Stowing Strategy For Inventory Replenishment In Automated Storage And Retrieval System

An Innovative Robotics Stowing Strategy For Inventory Replen...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Zheng-Hao Chong Ramamoorthy Luxman Wee-Ching Pang Zhao Yi Ren Meixuan Hendra Suratno Tju Albert Causo I-Ming Chen Robotics Research Centre Nanyang Technological University Singapore

Modern automated warehouses are equipped with one or many expensive and sophisticated equipment, such as palletizing robots, automated guided vehicles as well as an automated storage and retrieval system (AS/RS). these equipment are operated manually at many levels. these manual interruptions are accompanied by disadvantages of slow storage and retrieval speed, high operating costs and high frequency of errors in the operations. this paper presents an approach for efficient robotic stowing of items for inventory replenishment in a storage system. the objective is to enable a robotic arm system to stow items into a storage bin system and automatically generate a file to indicate which bin each object is stowed to. this would require a robust object recognition imbued with recognition history such that a previously recognized object is remembered as being stowed, even if it has been obscured by other objects subsequently during the task. A feature confidence aggregation strategy has been implemented to analyze a sequence of images containing a number of objects that are added to the storage system sequentially. the strategy is based on a weighted aggregation of ranked machine-learned classification scores and feature-matching recognition scores. this method is able to produce a high recognition rate and has been applied in the Amazon Robotic Challenge 2017 by Team Nanyang.

关键词： Manipulators Cameras Object recognition Databases Hardware Robot vision systems

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Vehicle Detection and Width Estimation in Rain by Fusing Radar and vision

Vehicle Detection and Width Estimation in Rain by Fusing Rad...

引用

international conference on control, automation, robotics and vision

作者： Jian-Gang Wang Simon Jian Chen Lu-Bing Zhou Kong-Wah Wan Wei-Yun Yau Robotics Department Institute for Infocomm Research Singapore Desay SV Automotive Singapore nuTonomy Singapore

While much effort has been devoted to deep learning object detection, relatively limited attention has been paid to object detection in bad weather, e.g. rain, snow or haze. In heavy rain, the raindrop on the front windshield can make it difficult to detect object from an in-car camera. the conventional way to cope with this has been to use radar as the main detection sensor. However, radar is highly susceptible to false positives. Furthermore, many entry level radar sensors only return the centroid of each detected object, rather than its size and extent. In addition, due to lack of texture input, radar cannot discriminate a vehicle from a non-vehicle object, e.g. roadside pole. this motivates us to detect vehicle by fusing radar and vision. In this paper, we first calibrate the radar and camera with respect to the ground plane. the radar detections are then projected to the camera image for target width estimation. Empirical evaluation on a large database shows that there is a natural synergy in both sensors, as the image based estimation is found to be greatly facilitated by the accuracy of the radar detection.

关键词： Radar imaging Radar detection Cameras Rain Calibration Vehicle detection

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VLO: vision-Laser Odometry for Autonomous Flight of Micro Aerial Vehicle

VLO: Vision-Laser Odometry for Autonomous Flight of Micro Ae...

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international conference on control, automation, robotics and vision

作者： Dengqing Tang Qiang Fang Lincheng Shen Tianjiang Hu College of Intelligence Science and Engineering National University of Defense Technology Changsha China School of Aeronautics and Astronautics Sun Yat-Sen University Guangzhou China

this paper presents an onboard micro aerial vehicle (MAV) localization algorithm VLO using onboard multi-sensor system consisting of a camera, a laser scanner and an inertial measurement unit. On the basis of onboard processor, the VLO can operate in real time without any prior information and ground assistance. Besides, it shows a strong robustness since it can work in both small and large, indoor and outdoor environment. As the main sensing devices of this system, the camera and laser scanner generate different characteristic data. the VLO fuses these two kinds of data for a more sufficient information about environment. A filter and an optimizer are then designed to estimate the MAV poses with extra onboard sensors data. Finally, an incremental dense map is updated. Different with vision-based or laser-based odometry, this system has no requirements for environments such as strong texture or structured surroundings. the Gazebo-based simulated and real MAV systems are built together for algorithm validation. the simulated and real results show that our onboard odometry VLO performs strong robustness without any prior information and basic assumptions.

关键词： Cameras Estimation Sensors Robustness Kalman filters Feature extraction Optical filters

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Real-Time Robot vision on Low-Performance Computing Hardware

Real-Time Robot Vision on Low-Performance Computing Hardware

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international conference on control, automation, robotics and vision (ICARCV)

作者： Gongjin Lan Jesús Benito-Picazo Diederik M. Roijers Enrique Domínguez A.E. Eiben Department of Computer Science VU University Amsterdam The Netherlands Department of Computer Languages and Computer Science University of Málaga Spain

Small robots have numerous interesting applications in domains like industry, education, scientific research, and services. For most applications vision is important, however, the limitations of the computing hardware make this a challenging task. In this paper, we address the problem of real-time object recognition and propose the Fast Regions of Interest Search (FROIS) algorithm to quickly find the ROIs of the objects in small robots with low-performance hardware. Subsequently, we use two methods to analyze the ROIs. First, we develop a Convolutional Neural Network on a desktop and deploy it onto the low-performance hardware for object recognition. Second, we adopt the Histogram of Oriented Gradients descriptor and linear Support Vector Machines classifier and optimize the HOG component for faster speed. the experimental results show that the methods work well on our small robots with Raspberry Pi 3 embedded 1.2 GHz ARM CPUs to recognize the objects. Furthermore, we obtain valuable insights about the trade-offs between speed and accuracy.

关键词： Robots Object recognition Image color analysis Real-time systems Support vector machines Hardware Graphics processing units

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Bluetooth Low Energy Based Activity Tracking of Patient

Bluetooth Low Energy Based Activity Tracking of Patient

引用

international conference on control, automation, robotics and vision

作者： Nasreen Mohsin Shahram Payandeh Derek Ho Jean Pierre Gelinas Networked Robotics and Sensing Laboratory Simon Fraser University Burnaby BC Canada Xerus Medical Inc. SFU Venture Labs Vancouver BC Canada

With growing demand for health care, medical clinicians and researchers need to gather quality patient centric data in order to better develop tools to improve quality of life. As an example, it has been a challenge to accurately evaluate postoperative mobility in patient care units. this paper describes the development and implementation of a proof-of-concept, a low cost and easily deployable Bluetooth Low Energy (BLE) based localization system used to identify and locate patients. Using the received signal strength (RSS) of BLE signal and the uniqueness of BLE hardware addresses, patients can be identified and located within the hospital room. the value of RSS of BLE signal from wearable BLE beacon varies with distance to the wall anchored BLE scanner. the first part of the paper presents the results of the experiments conducted in a low noise and non-reflective environment. the second half of the paper talks about the proposed algorithm of localization of the subject within the experimental room. the paper evaluates the effects of the number of BLE wall anchored scanners and the number of packets on the accuracy of the proposed algorithm. the proposed algorithm was evaluated to have better performance than other classical approaches.

关键词： Bluetooth Low Energy indoor positioning system

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Adaptive model predictive control for energy-efficient smart homes using a dynamic Kalman filter-bank

Adaptive model predictive control for energy-efficient smart...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Michaela Killian Martin Kozek Institute of Mechanics and Mechatronics Vienna University of Technology Vienna Austria

In order to be economically attractive the implementation of a smart home product should be simple and possibly done by craftsman. However, the model parametrization requires expert knowledge and the first commissioning has to perform well from the start. In this paper an adaptive model predictive control (aMPC) scheme for energy-efficient smart homes using a dynamic Kalman filter-bank is presented, which does not require on-line input from experts. Predefined smart home models are stored and simulated in parallel in the Kalman filter-bank. this allows for an efficient representation of the most important smart home dynamics and guarantees that the underlying aMPC model gives sufficient performance. the proposed scheme allows fast commissioning in smart homes and adds flexibility by extending the model set with additional predefined models. Switching models in the aMPC can represent time-varying behavior and ensure the best performance and energy-efficiency. the switching utilizes a hysteresis to avoid high-frequency switching and ensures a robust control scheme. Stability is analyzed, a simulation comparison to a Fuzzy MPC is performed, and the results show that the proposed method is both versatile and effective.

关键词： Adaptation models Kalman filters Smart homes Switches Hysteresis Optimized production technology

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$k^{3}$-Sparse Graph Convolutional Networks for Face Recognition

$k^{3}$-Sparse Graph Convolutional Networks for Face Recogni...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Renjie Wu Sei-ichiro Kamata Information Production and Systems Research Center (IPSRC) Waseda University Japan Graduate School of Information Production and Systems (IPS) Waseda University Japan

In recent years, deep learning networks have substantially improved the performance of face recognition. Although deep learning networks have been very successful, there are limited to underlying Euclidean structure data. When dealing with complex signals such as medical imaging, genetics, social networks and computer vision, recently there has been a growing interest in trying to apply learning on non-Euclidean geometric data. Graph convolutional networks are a new deep learning architecture for analyzing non-Euclidean geometric data. In computer vision, a human face image is modeled as a graph in the irregular domain. A major technical challenge is how to optimize the structured face graph. Because, classification performance critically depends on the quality of the graph. In this paper, we explore an undirected graph convolutional network called k 3 -SGCNs (k 3 -sparse graph convolutional networks). the main idea is to use sparsity-constrained optimization that obtain connected sparse subgraphs. A sparse graph of face image is composed of connected sparse subgraphs. Experiments demonstrate that the learned sparse graph has better performance than mutual k-nearest neighbor graph and l1 graph.

关键词： Silicon Face Face recognition Convolution Optimization Computer vision

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