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Cheng Z, Li B, Liu B. Research on Path Planning of Mobile Robot Based on Dynamic Environment. 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) 2022. [DOI: 10.1109/icma54519.2022.9856220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Zhixiang Cheng
- Tianjin University of Technology,Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Mechanical Engineering,Tianjin,China,300384
| | - Bin Li
- Tianjin University of Technology,National Demonstration Center for Experimental and Electrical Engineering Education,Tianjin,China
| | - Bin Liu
- Tianjin University of Technology,National Demonstration Center for Experimental and Electrical Engineering Education,Tianjin,China
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Saxena A, Chiu CY, Shrivastava R, Menke J, Sastry S. Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3181409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Amay Saxena
- Department of EECS at the University of California, Berkeley, CA, USA
| | - Chih-Yuan Chiu
- Department of EECS at the University of California, Berkeley, CA, USA
| | | | - Joseph Menke
- Department of EECS at the University of California, Berkeley, CA, USA
| | - Shankar Sastry
- Department of EECS at the University of California, Berkeley, CA, USA
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Su D, Kong H, Sukkarieh S, Huang S. Necessary and Sufficient Conditions for Observability of SLAM-Based TDOA Sensor Array Calibration and Source Localization. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2021.3069140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Miao Z, Liu YH, Wang Y, Chen H, Zhong H, Fierro R. Consensus With Persistently Exciting Couplings and Its Application to Vision-Based Estimation. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2801-2812. [PMID: 31180884 DOI: 10.1109/tcyb.2019.2918796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The problem of consensus in networked agent systems is revisited and applied to vision-based localization. A class of new consensus dynamics is introduced first, and sufficient conditions including the persistence of excitation on the coupling matrix for reaching consensus are derived. As an application of the proposed consensus dynamics, an adaptive localization algorithm then is proposed for autonomous robots equipped with primarily visual sensors in GPS-denied environments. In the context of consensus over an undirected tree topology, the convergence of the proposed localization algorithm is proved. Finally, both numerical simulations and physical experiments are presented to show the effectiveness of the proposed localization algorithm. Our algorithm is simpler to implement and computationally cheaper compared to other localization methods. Moreover, it is immune to error accumulation and long-term stable, and the asymptotical convergence of the estimation errors can be theoretically guaranteed.
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Wang K, Ma S, Chen J, Ren F, Lu J. Approaches Challenges and Applications for Deep Visual Odometry Toward to Complicated and Emerging Areas. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.3038898] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Xia L, Cui J, Shen R, Xu X, Gao Y, Li X. A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420919185] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
As one of the typical application-oriented solutions to robot autonomous navigation, visual simultaneous localization and mapping is essentially restricted to simplex environmental understanding based on geometric features of images. By contrast, the semantic simultaneous localization and mapping that is characterized by high-level environmental perception has apparently opened the door to apply image semantics to efficiently estimate poses, detect loop closures, build 3D maps, and so on. This article presents a detailed review of recent advances in semantic simultaneous localization and mapping, which mainly covers the treatments in terms of perception, robustness, and accuracy. Specifically, the concept of “semantic extractor” and the framework of “modern visual simultaneous localization and mapping” are initially presented. As the challenges associated with perception, robustness, and accuracy are being stated, we further discuss some open problems from a macroscopic view and attempt to find answers. We argue that multiscaled map representation, object simultaneous localization and mapping system, and deep neural network-based simultaneous localization and mapping pipeline design could be effective solutions to image semantics-fused visual simultaneous localization and mapping.
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Affiliation(s)
- Linlin Xia
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Jiashuo Cui
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Ran Shen
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Xun Xu
- Institute for Superconducting and Electronic Materials, University of Wollongong, Wollongong, Australia
| | - Yiping Gao
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Xinying Li
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments. COMPUTERS 2019. [DOI: 10.3390/computers8030063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.
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Su D, Vidal-Calleja T, Miro JV. Asynchronous microphone arrays calibration and sound source tracking. Auton Robots 2019. [DOI: 10.1007/s10514-019-09885-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lahemer ES, Rad A. An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments. SENSORS 2019; 19:s19122795. [PMID: 31234441 PMCID: PMC6630515 DOI: 10.3390/s19122795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/17/2019] [Accepted: 06/19/2019] [Indexed: 11/22/2022]
Abstract
In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.
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Affiliation(s)
- Elfituri S Lahemer
- Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada.
| | - Ahmad Rad
- Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada.
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Integration of Low-Cost GNSS and Monocular Cameras for Simultaneous Localization and Mapping. SENSORS 2018; 18:s18072193. [PMID: 29986515 PMCID: PMC6068946 DOI: 10.3390/s18072193] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 06/23/2018] [Accepted: 07/05/2018] [Indexed: 11/25/2022]
Abstract
Low-cost Global Navigation Satellite System (GNSS) receivers and monocular cameras are widely used in daily activities. The complementary nature of these two devices is ideal for outdoor navigation. In this paper, we investigate the integration of GNSS and monocular camera measurements in a simultaneous localization and mapping system. The proposed system first aligns the coordinates between two sensors. Subsequently, the measurements are fused by an optimization-based scheme. Our system can function in real-time and obtain the absolute position, scale, and attitude of the vehicle. It achieves a high accuracy without a preset map and also has the capability to work with a preset map. The system can easily be extended to create other forms of maps or for other types of cameras. Experimental results on a popular public dataset are presented to validate the performance of the proposed system.
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López E, García S, Barea R, Bergasa LM, Molinos EJ, Arroyo R, Romera E, Pardo S. A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments. SENSORS 2017; 17:s17040802. [PMID: 28397758 PMCID: PMC5422163 DOI: 10.3390/s17040802] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 03/27/2017] [Accepted: 04/05/2017] [Indexed: 11/29/2022]
Abstract
One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calculates the pose and environment map of different low-cost commercial aerial platforms, whose onboard computing capacity is usually limited. The proposed system adapts to the sensory configuration of the aerial robot, by integrating different state-of-the art SLAM methods based on vision, laser and/or inertial measurements using an Extended Kalman Filter (EKF). To do this, a minimum onboard sensory configuration is supposed, consisting of a monocular camera, an Inertial Measurement Unit (IMU) and an altimeter. It allows to improve the results of well-known monocular visual SLAM methods (LSD-SLAM and ORB-SLAM are tested and compared in this work) by solving scale ambiguity and providing additional information to the EKF. When payload and computational capabilities permit, a 2D laser sensor can be easily incorporated to the SLAM system, obtaining a local 2.5D map and a footprint estimation of the robot position that improves the 6D pose estimation through the EKF. We present some experimental results with two different commercial platforms, and validate the system by applying it to their position control.
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Affiliation(s)
- Elena López
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Sergio García
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Rafael Barea
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Luis M Bergasa
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Eduardo J Molinos
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Roberto Arroyo
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Eduardo Romera
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
| | - Samuel Pardo
- Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.
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Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age. IEEE T ROBOT 2016. [DOI: 10.1109/tro.2016.2624754] [Citation(s) in RCA: 1565] [Impact Index Per Article: 195.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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