Article
Automation & Control Systems
Pengcheng Shi, Zhikai Zhu, Shiying Sun, Xiaoguang Zhao, Min Tan
Summary: In this article, the invariant extended Kalman filter (EKF) is extended to LiDAR-inertial odometry and mapping systems using invariant observer design and the theory of Lie groups for directly fusing LiDAR and IMU measurements. Two independent systems, Inv-LIO1 and Inv-LIO2, are implemented to consider this from different aspects. Inv-LIO1 provides pure odometry with higher accuracy than other state-of-the-art systems, while Inv-LIO2 achieves superior accuracy in the map-refined odometry comparison.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Aerospace
Matthew W. Givens, Jay W. McMahon
Summary: This work develops a novel sequential information filter formulation for computationally efficient visual-inertial odometry and mapping. By carefully constructing the square-root information matrix, the mean and covariance can be easily and accurately recovered throughout operation. Results show that this filter does not require explicit marginalization of past landmark states to maintain constant-time complexity.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2023)
Article
Automation & Control Systems
Xinyu Jiang, Heng Li, Chuangquan Chen, Yongquan Chen, Junlang Huang, Zuguang Zhou, Yimin Zhou, Chi-Man Vong
Summary: This article proposes a tightly coupled direct depth-inertial odometry and mapping (DDIO-Mapping) framework for accurate localization and pose estimation in low-texture environment. The framework combines grayscale and depth features for optimization, improves searching efficiency with a new RGBD feature extraction method, and tackles imbalanced feature extraction with a feature filtering and selection strategy. Experimental results show that DDIO-Mapping reduces the root-mean-square error and maintains the same efficiency compared to other algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Robotics
Xinghan Li, Haodong Jiang, Xingyu Chen, He Kong, Junfeng Wu
Summary: Pose estimation is crucial for robots and can be achieved through filter-based methods. This research establishes a closed-form formula for error propagation in the presence of random noise using the Invariant extended Kalman filter (IEKF) and applies it to vision-aided inertial navigation. Numerical simulations and experiments demonstrate that the algorithm outperforms other state-of-the-art filter-based methods in specific parameter settings.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Chemistry, Analytical
Chaoyong Yang, Zhenhao Cheng, Xiaoxue Jia, Letian Zhang, Linyang Li, Dongqing Zhao
Summary: This paper proposes a fused 5G/geomagnetism/VIO indoor localization method to improve the accuracy and reliability of global positioning by integrating different sensor signals. The experimental results show that this method overcomes the problem of low accuracy in single sensor localization and provides more accurate global positioning results.
Article
Automation & Control Systems
Lei Wang, Shicheng Xia, Hengliu Xi, Shuangxi Li, Le Wang
Summary: This paper presents an interactive multi-model MSCKF algorithm based on multi-state constraint Kalman filter for visual-inertial odometry (VIO) tasks in unknown environments. Experimental results show that the proposed algorithm achieves better estimation accuracy and robustness, and is capable of long-term, high-precision, and consistent real-time VIO tasks.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Robotics
Yulin Yang, Chuchu Chen, Woosik Lee, Guoquan Huang
Summary: This paper proposes two novel algorithms that maintain system consistency by leveraging invariant state representation and ensure efficiency by decoupling features from covariance propagation. Monte-Carlo simulations and real world evaluations demonstrate that the proposed algorithms achieve higher accuracy compared to existing algorithms.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Hyunjun Lim, Jinwoo Jeon, Hyun Myung
Summary: This research proposes an unconstrained line-based SLAM method called UV-SLAM that utilizes vanishing points for structural mapping, aiming to solve the problems encountered in using line re-projection measurement model. By using vanishing points obtained from line features, this method improves localization accuracy and mapping quality.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Yuanqing Wu, Jiajun Zhao
Summary: In this article, a system is proposed to achieve high precision localization and mapping in large-scale environment by fusing multiple measurements. The system uses the iterated error-state Kalman filter to fuse IMU and LiDAR measurements, and also includes real-time GPS outlier detection method and LiDAR-based loop-closure detector. The experimental results demonstrate the robustness and accuracy of the method in completing the task of localization and mapping.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Chemistry, Analytical
Jung-Cheng Yang, Chun-Jung Lin, Bing-Yuan You, Yin-Long Yan, Teng-Hu Cheng
Summary: The Real-Time LiDAR Inertial Odometer System (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for feedback control of UAVs in an indoor environment. Compared to traditional methods, RTLIO has a more efficient initialization process and can outperform other methods in terms of time delay and position accuracy.
Article
Robotics
Yibin Wu, Jian Kuang, Xiaoji Niu, Jens Behley, Lasse Klingbeil, Heiner Kuhlmann
Summary: A reliable pose estimator for mobile robots is desired, which is robust to environmental disturbances. Inertial measurement units (IMUs) are important for perceiving the full motion state of the vehicle independently, but suffer from accumulative error. We propose to exploit the environmental perception ability of Wheel-INS to achieve SLAM using only one IMU, significantly improving positioning accuracy.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Zhuqing Zhang, Yang Song, Shoudong Huang, Rong Xiong, Yue Wang
Summary: This article presents a consistent and efficient filter for visual-inertial localization with a prebuilt map. A new Lie group and its algebra are proposed for designing a novel invariant extended Kalman filter (invariant EKF). To consider the uncertainty of map information, a Schmidt filter is introduced. Moreover, an easily implemented observability-constrained technique is introduced to maintain the correct observability properties of the system. Extensive simulations and real-world experiments validate the high consistency, accuracy, and efficiency of the proposed system.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Computer Science, Information Systems
Jung Min Pak
Summary: SEKFB is a new filtering algorithm that addresses the issue of uncertain process-noise covariance in indoor localization using constant-velocity motion models. It runs multiple EKFs in parallel with different covariance hypotheses, selecting the most probable output using Mahalanobis distance evaluation, and has been shown through simulations to provide accurate and reliable localization without the need for careful selection of process-noise covariance.
Article
Automation & Control Systems
Pieter van Goor, Robert Mahony, Tarek Hamel, Jochen Trumpf
Summary: This paper presents a novel approach to VSLAM by lifting the observer design to a novel Lie group VSLAM(n)(3) on which the system output is equivariant, leading to the design of a non-linear observer with almost semi-globally asymptotically stable error dynamics. Experimental results demonstrate the performance of the proposed observer is promising.
Article
Computer Science, Artificial Intelligence
Fabrizio Romanelli, Francesco Martinelli, Simone Mattogno
Summary: This paper discusses a solution to the Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) and Ultra Wide Band (UWB) antennas. The proposed approach utilizes a switching observer and a Robust EKF algorithm to achieve comparable performance to a VO algorithm even before closing the loop. It also includes a resilient module to evaluate the reliability of the position estimation. The approach is robust to unmodeled disturbances and adapts to sensor failures.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2023)