With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data - from both a hand held mobile phone and from a drone - we verify the performance of the proposed method.
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
Artificial Intelligence Research Institute, Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心人工智能研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；HSE University（高等经济大学）
A Multi-Task Deep Learning Framework for Building Footprint Segmentation
Center for Satellite Communications and Remote Sensing, Istanbul Technical University（伊斯坦布尔技术大学卫星通信与遥感中心）；Geomatics Engineering Department, Istanbul Technical University（伊斯坦布尔技术大学地理信息工程系）；Institute of Informatics, Satellite Communication and Remote Sensing Program, Istanbul Technical University（伊斯坦布尔技术大学信息学研究所卫星通信与遥感项目）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；ETH Zurich（苏黎世联邦理工学院）；International Committee of the Red Cross (ICRC)（红十字国际委员会（ICRC））；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis
University of Information Technology（信息技术大学）；Viet Nam National University Ho Chi Minh City（胡志明市越南国立大学）；ETIS (UMR 8051), CY Cergy Paris University, ENSEA, CNRS, France（法国赛尔齐巴黎大学、ENSEA、法国国家科学研究中心的ETIS（UMR 8051））
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所（CIRES））；Institute of Behavioral Science（行为科学研究所）
LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery
City University of Hong Kong（香港城市大学）；University of the Chinese Academy of Sciences（中国科学院大学）；University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；University of Southampton（南安普顿大学）
