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
Engineering, Electrical & Electronic
Xin Liu, Lan Cheng, Yapeng Yang, Gaowei Yan, Xinying Xu, Zhe Zhang
Summary: This work presents an autonomous framework for alpha/beta radiation mapping using a mobile robot equipped with LiDAR and a nuclear-radiation-detection sensor. The proposed method demonstrates effectiveness in different radiation scenarios for both indoor and outdoor environments.
Article
Computer Science, Information Systems
Baifan Chen, Siyu Li, Haowu Zhao, Limei Liu
Summary: A novel map merging method based on a suppositional box constructed by right-angled points and vertical lines is proposed. This method effectively merges maps in different scenes and achieves a successful matching rate higher than other features.
Review
Environmental Sciences
Shuran Zheng, Jinling Wang, Chris Rizos, Weidong Ding, Ahmed El-Mowafy
Summary: The Simultaneous Localization and Mapping (SLAM) technique has made significant progress in recent decades and has attracted considerable attention in the autonomous driving community. This study provides an overview of different SLAM implementation approaches and discusses the applications, challenges, and solutions in the context of autonomous driving. It also presents a real-world road test showcasing a multi-sensor-based SLAM procedure for autonomous driving.
Article
Computer Science, Hardware & Architecture
Petros Kapsalas, Aris S. Lalos, Dimitrios Serpanos, Konstantinos Moustakas
Summary: SLAM involves mapping an environment and estimating sensor motion simultaneously, requiring modular architectures for widespread adoption in emerging mobile computing systems.
Article
Automation & Control Systems
Hae Min Cho, HyungGi Jo, Euntai Kim
Summary: This article proposes a novel SLAM method named SP-SLAM using surfels as features to handle both high texture and low texture environments. The method can represent spacious environments with relatively small memory usage and achieves better performance in both types of environments compared to previous feature-based visual SLAM methods.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Hae Min Cho, HyungGi Jo, Euntai Kim
Summary: In this article, a novel SP-SLAM method using surfels as features for both high and low texture environments is proposed, with new objective functions to simultaneously optimize points, surfels, and cameras, demonstrating better performance and lower memory usage compared to previous feature-based visual SLAM methods.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Robotics
Chao Xia, Chenfeng Xu, Patrick Rim, Mingyu Ding, Nanning Zheng, Kurt Keutzer, Masayoshi Tomizuka, Wei Zhan
Summary: Current LiDAR odometry, mapping, and localization methods based on point-wise representations of 3D scenes face space-inefficiency issues. To address this, we propose a novel method that describes scenes using compact quadric surface representations instead of point clouds. Our method segments the point cloud into patches and fits each patch to a quadric implicit function, providing a more efficient representation. We also introduce an incremental growing method that eliminates the need for repeated fitting. Experimental results demonstrate that our method achieves competitive accuracy with low latency and memory usage.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Environmental Sciences
Yuan Lin, Haiqing Dong, Wentao Ye, Xue Dong, Shuogui Xu
Summary: This work presents an information-based landmarks assisted simultaneous localization and mapping (InfoLa-SLAM) method using single-line lidar in large-scale scenes. The proposed approach reduces the computational cost of SLAM and achieves accurate mapping through a keyframe selection method based on Fisher information and an efficient global descriptor for place recognition. The results show a significant reduction in the number of keyframes, high probability of relocalization correction, and lightweight performance compared to traditional strategies.
Article
Robotics
Hriday Bavle, Jose Luis Sanchez-Lopez, Muhammad Shaheer, Javier Civera, Holger Voos
Summary: In this paper, an evolved version of Situational Graphs called S-Graphs+ is presented, which jointly models a pose graph and a 3D scene graph in a single optimizable factor graph. S-Graphs+ includes four layers: keyframes, walls, rooms, and floors, and is optimized in real-time to achieve accurate robot pose estimation and map construction. Novel room and floor segmentation algorithms utilizing mapped wall planes and free-space clusters are also introduced. Experimental results demonstrate that S-Graphs+ outperforms other methods in terms of accuracy and scene modeling capabilities. The software is made available as a docker file.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Xi Zheng, Rui Ma, Rui Gao, Qi Hao
Summary: In this study, a phase-based SLAM framework is proposed for fast and accurate estimation of SLI sensor pose and 3D object reconstruction. By developing a reprojection model, a local optimizer, and a compressive phase comparison method, phase registration with low computational complexity and efficient loop closure detection are achieved. The experimental results demonstrate that the proposed Phase-SLAM outperforms other methods in terms of efficiency and accuracy.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Dengxiang Chang, Runbang Zhang, Shengjie Huang, Manjiang Hu, Rongjun Ding, Xiaohui Qin
Summary: Accurate localization is crucial for autonomous driving systems, and LiDAR is commonly used due to its reliability. This paper proposes a robust and accurate LiDAR SLAM that innovates feature point extraction and motion constraint construction. Feature points are extracted using adaptive point roughness evaluation and outliers are removed with a dynamic threshold filter. Motion constraint construction uses weighted bimodal least squares to optimize the relative pose between current frame and point map. The solution achieves better performance in terms of accuracy and robustness according to multiple datasets.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Chongjian Yuan, Wei Xu, Xiyuan Liu, Xiaoping Hong, Fu Zhang
Summary: This study proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The method utilizes voxel maps to probabilistically represent the environment and accurately register new LiDAR scans, achieving high accuracy and efficiency compared to other state-of-the-art methods.
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
Robotics
Kenji Koide, Masashi Yokozuka, Shuji Oishi, Atsuhiko Banno
Summary: The letter introduces a real-time 3D LiDAR mapping framework based on global matching cost minimization, which leverages GPU parallel processing to improve estimation accuracy of long trajectories by directly minimizing matching costs between frames over the entire map.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Runwei Guan, Shanliang Yao, Lulu Liu, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee, Yutao Yue
Summary: With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant. Traditional RGB cameras cannot work effectively in adverse weather and at night, which has led to the emergence of 4D millimeter-wave radar as a new perception sensor. However, the radar suffers from water-surface clutter and irregular shape of point cloud. To address these issues, this paper proposes a high-performance panoptic perception model called Mask-VRDet, which fuses features of vision and radar using graph neural network.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Adrien Le Reun, Kevin Subrin, Anthony Dubois, Sebastien Garnier
Summary: This study aims to evaluate the quality and health of aerospace parts using a high-dimensional robotic cell. By utilizing X-ray Computed Tomography devices, the interior of the parts can be reconstructed and anomalies can be detected. A methodology is proposed to assess both the raw process capability and the improved process capability, with three strategies developed to improve the robot behavior model and calibration.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Weiming Ba, Jung-Che Chang, Jing Liu, Xi Wang, Xin Dong, Dragos Axinte
Summary: This paper proposes a hybrid scheme for kinematic control of continuum robots, which avoids errors through tension supervision and accurate piecewise linear approximation. The effectiveness of the controller is verified on different continuum robotic systems.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, Antonio Paolillo
Summary: In this study, a learning-based approach is proposed to predict the probability of human users interacting with a robot before the interaction begins. By considering the pose and motion of the user, the approach labels the robot's encounters with humans in a self-supervised manner. The method is validated and deployed in various scenarios, achieving high accuracy in predicting user intentions to interact with the robot.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Tiago Cortinhal, Eren Erdal Aksoy
Summary: This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation between LiDAR and camera sensors. The model is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments, and it has practical applications in fields like autonomous vehicles.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis
Summary: This paper addresses the issue of informative path planning for a UAV used in precision agriculture. By using a non-uniform scanning approach, the time spent in areas with minimal value is reduced, while maintaining high precision in information-dense regions. A novel active sensing and deep learning-based coverage path planning approach is proposed, which adjusts the UAV's speed based on the quantity and confidence level of identified plant classes.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shota Kokubu, Pablo E. Tortos Vinocour, Wenwei Yu
Summary: In this study, a new modular soft actuator was proposed to improve the support performance of soft rehabilitation gloves (SRGs). Objective evaluations and clinical tests were conducted to demonstrate the effectiveness and functionality of the proposed actuator and SRG.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Jinliang Zhu, Yuanxi Sun, Jie Xiong, Yiyang Liu, Jia Zheng, Long Bai
Summary: This paper proposes an active prosthetic knee joint with a variable stiffness parallel elastic actuation mechanism. Numerical verifications and practical experiments demonstrate that the mechanism can reduce torque and power, thus reducing energy consumption and improving the endurance of the prosthetic knee joint.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Yong You, Jingtao Wu, Yunlong Meng, Dongye Sun, Datong Qin
Summary: A new power-cycling variable transmission (PCVT) is proposed and applied to construction vehicles to improve transmission efficiency. A shift correction strategy is developed based on identifying the changes in construction vehicles' mass and gradient. Simulation results show that the proposed method can correct shift points, improve operation efficiency, and ensure a safer operation process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shaorui Liu, Wei Tian, Jianxin Shen, Bo Li, Pengcheng Li
Summary: This paper proposes a two-objective optimization technique for multi-robot systems, addressing the issue of balancing productivity and machining performance in high-quality machining tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pengchao Ding, Faben Zhu, Hongbiao Zhu, Gongcheng Wang, Hua Bai, Han Wang, Dongmei Wu, Zhijiang Du, Weidong Wang
Summary: We propose an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme includes stair localization, structural parameter estimation, and optimization of the approaching process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pedro Azevedo, Vitor Santos
Summary: Accurate detection and tracking of vulnerable road users and traffic objects are vital tasks for autonomous driving and driving assistance systems. This paper proposes a solution for object detection and tracking in an autonomous driving scenario, comparing different object detectors and exploring the deployment on edge devices. The effectiveness of DeepStream technology and different object trackers is assessed using the KITTI tracking dataset.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Benjamin Beiter, Divya Srinivasan, Alexander Leonessa
Summary: Powered exoskeletons can significantly reduce physical workload and have great potential impact on future labor practices. To truly assist users in achieving task goals, a shared autonomy control framework is proposed to separate the control objectives of the human and exoskeleton. Positive Power control is introduced for the human-based controller, while 'acceptance' is used as a measure of matching the exoskeleton's control objective to the human's. Both control objectives are implemented in an optimization-based Whole-Body-Control structure. The results verify the effectiveness of the control framework and its potential for improving cooperative control for powered exoskeletons.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)