Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images
Joanneum Research - Robotics（约翰内姆研究中心-机器人学）；The Intervention Centre, Oslo University Hospital（奥斯陆大学医院介入中心）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）
Justinas Miseikis, Inka Brijacak, Saeed Yahyanejad, Kyrre Glette, Ole Jakob Elle, Jim Torresen
The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed setups, which have all the sensors fixed and calibrated in relation to the rest of the system. However, it becomes a limiting factor when the system needs to be reconfigured or moved. We present a deep learning approach, which aims to solve this issue. Our method learns to identify and precisely localise the robot in 2D camera images, so having a fixed setup is no longer a requirement and a camera can be moved. In addition, our approach identifies the robot type and estimates the 3D position of the robot base in the camera image as well as 3D positions of each of the robot joints. Learning is done by using a multi-objective convolutional neural network with four previously mentioned objectives simultaneously using a combined loss function. The multi-objective approach makes the system more flexible and efficient by reusing some of the same features and diversifying for each objective in lower layers. A fully trained system shows promising results in providing an accurate mask of where the robot is located and an estimate of its base and joint positions in 3D. We compare the results to our previous approach of using cascaded convolutional neural networks.
Techno-Economic analysis for Smart Hangar inspection operations through Sensing and Localisation at scale
Cranfield University（克兰菲尔德大学）；Centre for Assured And Connected Autonomy（可信与连接自主性中心）；Faculty of Engineering and Applied Sciences（工程与应用科学学院）；University of Southampton（南安普顿大学）；Department of Aeronautics and Astronautics（航空与航天系）
Distant Object Localisation from Noisy Image Segmentation Sequences
Research Council of Finland（芬兰研究理事会）；RCF Flagship Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE)（RCF旗舰森林-人类-机器交互——构建韧性，重新定义价值网络和赋能有意义体验（UNITE））
CNN-Based Camera Pose Estimation and Localisation of Scan Images for Aircraft Visual Inspection
Pillar of Engineering Product Development（工程产品发展中心）；Singapore University of Technology and Design（新加坡科技设计大学）；ST Engineering Aerospace Ltd.（ST工程航空航天有限公司）
MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters
Faculty of Engineering, University of Porto（波尔图大学工程学院）；INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência（INESC TEC – 系统与计算机工程、技术与科学研究所）；Khalifa University of Science, Technology, and Research（哈利法科技研究大学）
RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion
University of Edinburgh（爱丁堡大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Touchlab Limited（触觉实验室有限公司）；the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels
Department of Aeronautical and Automotive Engineering, Loughborough University（航空航天与汽车工程系，洛桑大学）；Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra（电气与计算机工程系，系统与机器人研究所，科英布拉大学）；Antobot Ltd, Arise Innovation Hub（Antobot公司，Arise创新中心）
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods
Oxford Robotics Inst., Dept. of Eng. Science, Univ. of Oxford, UK（牛津大学机器人研究所、工程科学系）；Group of Automation, Robotics and Computer Vision (AUROVA), University of Alicante, Spain（自动化、机器人与计算机视觉小组（AUROVA）、阿尔基兰特大学）
Adapting CNNs for Fisheye Cameras without Retraining
Australian Centre For Robotics (ACFR)（澳大利亚机器人中心）；School of Aerospace, Mechanical and Mechatronic Engineering（航空航天、机械与机电工程学院）；The University of Sydney（悉尼大学）
