Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.
DIVO: Continuous-time DVL-Inertial-Visual Odometry for Unmanned Underwater Vehicles
Voyis Imaging Inc.（Voyis影像公司）；Natural Sciences and Engineering Research Council of Canada (NSERC)（加拿大自然科学与工程研究理事会（NSERC））；McGill University（麦吉尔大学）；McGill Engineering Doctoral Award (MEDA) Program（麦吉尔大学工程博士奖（MEDA）项目）
LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map
Centre for Advanced Robotics Technology Innovation (CARTIN)（先进机器人技术创新中心）；School of Electrical and Electronic Engineering（电气与电子工程学院）；Nanyang Technological University（南洋理工大学）
VGC-RIO: A Tightly Integrated Radar-Inertial Odometry with Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；National Key Laboratory of Equipment State Sensing and Smart Support（装备状态感知与智能支撑国家重点实验室）
VIPS-Odom: Visual-Inertial Odometry Tightly-coupled with Parking Slots for Autonomous Parking
Advanced Micro Devices (AMD), Inc.（超威半导体公司（AMD））；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling
Department of Information Technology and Human Factors, the National Institute of Advanced Industrial Science and Technology（信息科技与人因工程系，国家先进工业科学与技术研究院）
University of Toronto（多伦多大学）；Delft University of Technology（代尔夫特理工大学）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；University College London（伦敦大学学院）
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
The Space Robotics Lab. in the Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（东京大学工学研究生院航空航天工程系空间机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
LIMOT: A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking
Tongji University（同济大学）；Institute of Intelligent Vehicles, Tongji University（同济大学智能车辆研究所）；School of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
Space Robotics Lab., Dept. Aerospace Eng., Tohoku University, Japan（东京东京大学航空航天工程系空间机器人实验室，日本）；Dept. of Information Technology and Human Factors, the National Institute of Advanced Industrial Science and Technology, Japan（日本国家先进工业科学和技术研究院信息科技与人因系）
FGO-ILNS: Tightly Coupled Multi-Sensor Integrated Navigation System Based on Factor Graph Optimization for Autonomous Underwater Vehicle
Sun Yat-sen University（中山大学）；Shenzhen Key Laboratory of Navigation and Communication Integration（深圳导航与通信融合重点实验室）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））
Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Shenzhen Unity Drive Innovation Technology Co., Ltd.（深圳Unity Drive创新科技有限公司）；College of Information Science and Technology, Eastern Institute of Technology（信息科学与技术学院，东方技术学院）
Learning to Anchor Visual Odometry: KAN-Based Pose Regression for Planetary Landing
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）
FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement
School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology（中国矿业大学江苏省智能采矿装备协同创新中心）；National Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology（中国矿业大学智能采矿装备技术全国重点实验室）
A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University（turbulence和复杂系统国家重点实验室，先进制造与机器人系，工程学院，北京大学）；Tencent RoboticsX（腾讯机器人科技）；College of Information and Electrical Engineering, China Agricultural University（信息与电气工程学院，中国农业大学）
