An analysis of the relative motion and point feature model configurations leading to solution degeneracy is presented, for the case of a Simultaneous Localization and Mapping system using multicamera clusters with non-overlapping fields-of-view. The SLAM optimization system seeks to minimize image space reprojection error and is formulated for a cluster containing any number of component cameras, observing any number of point features over two keyframes. The measurement Jacobian is transformed to expose a reduced-dimension representation such that the degeneracy of the system can be determined by the rank of a dense submatrix. A set of relative motions sufficient for degeneracy are identified for certain cluster configurations, independent of target model geometry. Furthermore, it is shown that increasing the number of cameras within the cluster and observing features across different cameras over the two keyframes reduces the size of the degenerate motion sets significantly.
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Mechanical and Power Engineering, East China University of Science and Technology（华东理工大学机械与动力工程学院）
MonoSLAM: Robust Monocular SLAM with Global Structure Optimization
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of International Education, Wuhan University of Technology（武汉理工大学国际教育学院）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic
COMSATS University Islamabad, Lahore Campus（COMSATS大学伊斯兰堡分校拉合尔校区）；Pakistan Navy Engineering College, National University of Sciences and Technology (NUST)（巴基斯坦国立科技大学巴基斯坦海军工程学院）
提出一种基于深度学习的多目标多摄像头跟踪框架，利用Mask R-CNN、迁移学习与Deep SORT等技术解决城市规模交通中的车辆跟踪难题，在AI City Challenge数据集上取得IDF1为0.8289的优异性能。
German Aerospace Center (DLR), Institute of Space Research（德国航空航天中心（DLR）空间研究所）；Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente（代尔夫特理工大学地理信息科学与地球观测学院）
Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；Advanced Laser Technology Lab of Anhui Province（安徽省先进激光技术实验室）；Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所空间主动光电技术重点实验室）
TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Neural Radiance Fields
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University（云南大学云南省智能系统与计算重点实验室）
MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments
school of Mechanical Engineering, Shandong University（山东大学机械工程学院）；State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人与系统国家重点实验室）；College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学机电学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
