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Sun RH, Zhao X, Wu CD, Zhang L, Zhao B. Research on Mobile Robot Navigation Method Based on Semantic Information. SENSORS (BASEL, SWITZERLAND) 2024; 24:4341. [PMID: 39001121 PMCID: PMC11244283 DOI: 10.3390/s24134341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024]
Abstract
This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.
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Affiliation(s)
- Ruo-Huai Sun
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (R.-H.S.)
- SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
| | - Xue Zhao
- Daniel L. Goodwin College of Business, Benedict University, Chicago, IL 60601, USA;
| | - Cheng-Dong Wu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (R.-H.S.)
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
| | - Lei Zhang
- SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
| | - Bin Zhao
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (R.-H.S.)
- SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
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Zhang L, Deng J. Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm. SENSORS (BASEL, SWITZERLAND) 2024; 24:3154. [PMID: 38794008 PMCID: PMC11124910 DOI: 10.3390/s24103154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications.
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Affiliation(s)
- Liang Zhang
- School of Electrical Engineering and Automation, Anhui University, Hefei 230093, China
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Zhang C, Yang Z, Xue B, Zhuo H, Liao L, Yang X, Zhu Z. Perceiving like a Bat: Hierarchical 3D Geometric-Semantic Scene Understanding Inspired by a Biomimetic Mechanism. Biomimetics (Basel) 2023; 8:436. [PMID: 37754187 PMCID: PMC10526479 DOI: 10.3390/biomimetics8050436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/28/2023] Open
Abstract
Geometric-semantic scene understanding is a spatial intelligence capability that is essential for robots to perceive and navigate the world. However, understanding a natural scene remains challenging for robots because of restricted sensors and time-varying situations. In contrast, humans and animals are able to form a complex neuromorphic concept of the scene they move in. This neuromorphic concept captures geometric and semantic aspects of the scenario and reconstructs the scene at multiple levels of abstraction. This article seeks to reduce the gap between robot and animal perception by proposing an ingenious scene-understanding approach that seamlessly captures geometric and semantic aspects in an unexplored environment. We proposed two types of biologically inspired environment perception methods, i.e., a set of elaborate biomimetic sensors and a brain-inspired parsing algorithm related to scene understanding, that enable robots to perceive their surroundings like bats. Our evaluations show that the proposed scene-understanding system achieves competitive performance in image semantic segmentation and volumetric-semantic scene reconstruction. Moreover, to verify the practicability of our proposed scene-understanding method, we also conducted real-world geometric-semantic scene reconstruction in an indoor environment with our self-developed drone.
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Affiliation(s)
| | - Zhong Yang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (C.Z.)
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Bavle H, Sanchez-Lopez JL, Cimarelli C, Tourani A, Voos H. From SLAM to Situational Awareness: Challenges and Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:4849. [PMID: 37430762 DOI: 10.3390/s23104849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 07/12/2023]
Abstract
The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.
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Affiliation(s)
- Hriday Bavle
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Jose Luis Sanchez-Lopez
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Claudio Cimarelli
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Ali Tourani
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Holger Voos
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
- Department of Engineering, Faculty of Science, Technology, and Medicine (FSTM), University of Luxembourg, 1359 Luxembourg, Luxembourg
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Wang S, Wang Y, Li D, Zhao Q. Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052399. [PMID: 36904602 PMCID: PMC10007377 DOI: 10.3390/s23052399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/12/2023]
Abstract
For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms.
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Affiliation(s)
- Shuo Wang
- School of Information, Renmin University of China, Beijing 100872, China
| | - Yongcai Wang
- School of Information, Renmin University of China, Beijing 100872, China
- Metaverse Research Center, Renmin University of China, Beijing 100872, China
| | - Deying Li
- School of Information, Renmin University of China, Beijing 100872, China
| | - Qianchuan Zhao
- Department of Automation, Tsinghua University, Beijing 100084, China
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Giubilato R, Sturzl W, Wedler A, Triebel R. Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3188118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Riccardo Giubilato
- German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Weßling, Germany
| | - Wolfgang Sturzl
- German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Weßling, Germany
| | - Armin Wedler
- German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Weßling, Germany
| | - Rudolph Triebel
- German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Weßling, Germany
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Chang Y, Ebadi K, Denniston CE, Ginting MF, Rosinol A, Reinke A, Palieri M, Shi J, Chatterjee A, Morrell B, Agha-mohammadi AA, Carlone L. LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3191204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yun Chang
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamak Ebadi
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | | | - Antoni Rosinol
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Matteo Palieri
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - Jingnan Shi
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arghya Chatterjee
- Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Benjamin Morrell
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Luca Carlone
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
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