Inertial motion capture systems widely use low-cost IMUs to obtain the orientation of human body segments, but these sensors alone are unable to estimate link positions. Therefore, this research used a SLAM method in conjunction with inertial data fusion to estimate link positions. SLAM is a method that tracks a target in a reconstructed map of the environment using a camera. This paper proposes quaternion-based extended and square-root unscented Kalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filters use measurements based on SLAM position data, multi-link biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the fusion algorithm is capable of estimating link geometries, allowing the imposing of biomechanical constraints without a priori knowledge of sensor positions. An optical tracking system is used as a reference of ground-truth to experimentally evaluate the performance of the proposed algorithm in various scenarios of human arm movements. The proposed algorithms achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude estimation. Compared to the EKF, the SRUKF algorithm presents a smoother and higher convergence rate but is 2.4 times more computationally demanding. After convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in position and attitude estimation, respectively. Using an absolute position measurement method instead of SLAM produced 80% and 40%, in the case of EKF, and 60% and 6%, in the case of SRUKF, less error in position and attitude estimation, respectively.
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM
POSTECH（浦项科技大学）；Kyungpook National University（庆北国立大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Korea University（高丽大学）
Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
Subsea Science and Technology Research Institute, Isfahan University of Technology, Isfahan, Iran（伊斯法罕技术大学下海科学与技术研究所，伊斯法罕，伊朗）；Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran（伊斯法罕技术大学电气与计算机工程系，伊斯法罕，伊朗）
EKF-Based Fusion of Wi-Fi/LiDAR/IMU for Indoor Localization and Navigation
School of Advanced Technology, Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学先进技术学院）；Department of Electrical Engineering and Electronics, University of Liverpool（利物浦大学电子工程系）；Zhejiang University of Technology（浙江工业大学）；School of Artificial Intelligence, Suzhou Vocational Institute of Industrial Technology（苏州工业职业技术学院人工智能学院）
Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights
CoE MARBLE - Centre of Excellence in Maritime Robotics and Technologies for Sustainable Blue Economy（MARBLE卓越中心 - 可持续蓝色经济海事机器人与技术卓越中心）；Universidad de Sevilla（塞维利亚大学）；University of Zagreb（萨格勒布大学）
