We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking, and ladder-climbing) and report mean test classification accuracy of over 90% on a dataset with five different subjects. From these motion types, we select two of the most common (walking and running), and describe a method to compute optimal zero-velocity detection parameters tailored to both a specific user and motion type by maximizing the detector F-score. By combining the motion classifier with a set of optimal detection parameters, we show how we can reduce INS position error during mixed walking and running motion. We evaluate our adaptive system on a total of 5.9 km of indoor pedestrian navigation performed by five different subjects moving along a 130 m path with surveyed ground truth markers.
Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
Autonomous Navigation and Sensor Fusion Lab（自主导航与传感器融合实验室）；Hatter Department of Marine Technologies（海洋技术系）；Charney School of Marine Sciences（海洋科学学院）；University of Haifa（海法大学）
Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
Robotics, Intelligent Systems, and Control Lab, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia（机器人、智能系统与控制实验室，国王阿卜杜勒-阿齐兹大学科学与技术学院（KAUST），沙特阿拉伯图瓦尔）
Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU
University of Bonn（波恩大学）；Institute of Geodesy and Geoinformation（大地测量与地理信息研究所）；Center for Robotics（机器人中心）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；GNSS Research Center（全球导航卫星系统研究中心）；Wuhan University（武汉大学）
Constrained Factor Graph Optimization for Robust Networked Pedestrian Inertial Navigation
Dept. of Aerospace Engineering and Mechanics, Univ. of Minnesota, Twin Cities, Minnesota 55455, USA（航空航天工程与力学系，明尼苏达大学，双城分校，明尼苏达州55455，美国）；Dept. of Electrical and Computer Engineering, Univ. of California, Riverside, CA 92521, USA（电气与计算机工程系，加州大学，河滨分校，加利福尼亚州92521，美国）
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU
Department of Engineering ``Enzo Ferrari'', University of Modena（工程系（恩佐·费拉里），莫德纳大学）；Department of Science and Methods for Engineering, University of Modena（工程科学与方法系，莫德纳大学）
Momentum Model-based Minimal Parameter Identification of a Space Robot
Indian Institute of Technology, Jodhpur, Rajasthan, India（印度理工学院，朱达普尔，拉贾斯坦邦，印度）；McGill University, Montreal, Quebec H3A 0C3, Canada（麦吉尔大学，蒙特利尔，魁北克 H3A 0C3，加拿大）
Recording Hand-Held Laparoscopic Instrument Motion in the Operating Room: Magnetometer-Free Fusion of Inertial, Range and Visual Sensing
Seoul National University Hospital（首尔大学医院）；Rosota Inc.（Rosota公司）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University（首尔大学）；Eulji University College of Medicine（乙支大学医学院）
Time-Varying Foot-Placement Control for Underactuated Humanoid Walking on Swaying Rigid Surfaces
College of Engineering, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校工程学院）；The Ohio State University（俄亥俄州立大学）；University of Michigan（密歇根大学）；School of Mechanical Engineering, Purdue University（普渡大学机械工程学院）
