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IEEE JOURNAL OF MICROWAVES 2025年第2期5卷 344-355页

作者： Koerner, Tobias Batra, Aman Kaiser, Thomas Pohl, Nils Schulz, Christian Rolfes, Ilona Barowski, Jan Ruhr Univ Bochum Inst Microwave Syst D-44801 Bochum Germany Univ Duisburg Essen Inst Digital Signal Proc D-47057 Duisburg Germany Ruhr Univ Bochum Inst Integrated Syst D-44801 Bochum Germany

simultaneous localization and mapping (SLAM) of indoor scenarios is usually based on camera or lidar sensors as data sources. However, radar based room scanning offers several complementary advantages to these systems. Among other features, radar sensors are more robust to optical opacities, for example, those caused by smoke and dust in emergency scenarios or in harsh environments. Furthermore, the coherent measurement principle of radar sensors provides highly precise distance information that can be utilized to track the exact position and dimensions of the visible objects. In contrast to camera and lidar, radar applications in room exploration are up to now limited by reduced spatial, i.e. mostly angular/lateral, resolution. This is due to the comparably large wavelength of the utilized signals. This work demonstrates the capabilities of ultrawideband millimeterwave Frequency Modulated Continuous Wave (FMCW) radar sensors operating around 80 GHz in conjunction with the Synthetic Aperture Radar (SAR) imaging method. To allow imaging whilst moving, self-localization techniques based on sub-aperture processing are evaluated. Therefore, we demonstrate a detailed mapping procedure for room exploration applications by exploiting large absolute bandwidths of more than 20 GHz with high resolution imaging techniques on a mobile robot platform.

关键词： Radar imaging Radar simultaneous localization and mapping Sensors Bandwidth Antennas Synthetic aperture radar Radar antennas Location awareness Imaging millimeterwave radar synthetic aperture radar simultaneous localization and mapping signal processing ultra wideband radar

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A 2.793 μW Near-Threshold Neuronal Population Dynamics Trajectory Filter for Reliable simultaneous localization and mapping

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1269-1281页

作者： Wei, Zhengzhe Dong, Boyi Su, Yuqi Wang, Yi Yang, Chuanshi Lu, Yuncheng Wang, Chao Kim, Tony Tae-Hyoung Zheng, Yuanjin Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Continental Automot Singapore Singapore 339780 Singapore Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

This work presents an algorithm hardware co-design implementing a digital neuronal population dynamics simulator intended for the trajectory error correction task within a simultaneous localization and mapping workflow. A custom discretized procedural algorithm approximating a neuronal population dynamics-based inference operation is developed for mapping onto an ultra-lightweight digital macro featuring massively parallel in-situ processing techniques. Fabricated using a 40 nm technology, the test chip features a 22 x 22 neuron array with 0.1358 mm(2) core area and provides a 12-bit computing precision. A time-multiplexed processing element design prevents the use of excessive silicon area. Accomplished via extensive data reuse through massively parallel processing-in-memory architecture attached to a custom I/O interface, a single inference operation is completed within 3277 clock cycles, providing 200 inferences per second operating at a low frequency of 0.667Mhz with a 0.5V core supply and consuming sub-10- mu W power.

关键词： simultaneous localization and mapping processing-in-memory trajectory filter finite-difference near-threshold computing ultra-low power edge processing

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Improving Sensor Adaptability and Functionality in Cartographer simultaneous localization and mapping

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SENSORS 2025年第6期25卷 1808-1808页

作者： Jeong, Wonseok Lee, Chanho Lee, Namyeong Hong, Seungwoo Kang, Donghyun An, Donghyeok Changwon Natl Univ Dept Comp Engn Chang Won 51140 South Korea Dept Future Digital Solut Volvo Construct Equipmen Chang Won 51706 South Korea Elect & Telecommun Res Inst ETRI Network Res Dept Daejeon 34129 South Korea Gachon Univ Coll IT Convergence Dept Comp Engn Seongnamsi 13120 South Korea

This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve this, we developed a novel SLAM framework that integrates five additional functionalities into the existing Google Cartographer and Robot Operating System (ROS). These innovations include an inertial data generation system and a sensor data preprocessing system to mitigate issues arising from various sensor configurations. Additionally, the framework enhances system utility through real-time 3D topographic mapping, multi-node SLAM capabilities, and elliptical sensor data filtering. The average execution times for sensor data preprocessing and virtual inertial data generation are 0.55 s and 0.15 milliseconds, indicating a low computational overhead. Elliptical filtering has nearly the same execution speed as the existing filtering scheme.

关键词： Cartographer simultaneous localization and mapping Robot Operating System multi-node real time

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Privacy-Preserved Visual simultaneous localization and mapping Based on a Dual-Component Approach

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2583-2583页

作者： Yang, Mingxu Huang, Chuhua Huang, Xin Hou, Shengjin Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point clouds can reconstruct original scenes. To address these concerns, this paper proposes a dual-component privacy-preserving approach for visual SLAM. First, a privacy protection method for images is proposed, which combines object detection and image inpainting to protect privacy-sensitive information in images. Second, an encryption algorithm is introduced to convert 3D point cloud data into a 3D line cloud through dimensionality enhancement. Integrated with ORB-SLAM3, the proposed method is evaluated on the Oxford Robotcar and KITTI datasets. Results demonstrate that it effectively safeguards privacy-sensitive information while ORB-SLAM3 maintains accurate pose estimation in dynamic outdoor scenes. Furthermore, the encrypted line cloud prevents unauthorized attacks on recovering the original point cloud. This approach enhances privacy protection in visual SLAM and is expected to expand its potential applications.

关键词： simultaneous localization and mapping privacy preserving dual-component approach image inpainting point dimensionality lifting

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Enhanced simultaneous localization and mapping Algorithm Based on Deep Learning for Highly Dynamic Environment

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SENSORS 2025年第8期25卷 2539-2539页

作者： Lu, Yin Wang, Haibo Sun, Jin Zhang, J. Andrew Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Univ Technol Sydney UTS Global Big Data Technol Ctr GBDTC Sydney NSW 2007 Australia

Visual simultaneous localization and mapping (SLAM) is a critical technology for autonomous navigation in dynamic environments. However, traditional SLAM algorithms often struggle to maintain accuracy in highly dynamic environments, where elements undergo significant, rapid, and unpredictable changes, leading to asymmetric information acquisition. Aiming to improve the accuracy of the SLAM algorithm in a dynamic environment, a dynamic SLAM algorithm based on deep learning is proposed. Firstly, YOLOv10n is used to improve the front end of the system, and semantic information is added to each frame of the image. Then, ORB-SLAM2 is used to extract feature points in each region of each frame and retrieve semantic information from YOLOv10n. Finally, through the map construction thread, the dynamic object feature points extracted by YOLOv10n are eliminated, and the construction of a static map is realized. The experimental results show that the accuracy of the proposed algorithm is improved by more than 96% compared with the state-of-the-art ORB-SLAM2 in a highly dynamic environment. Compared with other dynamic SLAM algorithms, the proposed algorithm has improved both accuracy and runtime.

关键词： deep learning simultaneous localization and mapping YOLOv10n semantic segmentation

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A Review of simultaneous localization and mapping Algorithms Based on Lidar

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第2期16卷 56-56页

作者： Li, Yong An, Jiexin He, Na Li, Yanbo Han, Zhenyu Chen, Zishan Qu, Yaping Jiangsu Univ Automot Engn Res Inst 301 Xuefu Rd Zhenjiang 212013 Peoples R China Beijing Inst Space Launch Technol Beijing 100076 Peoples R China Lingyun Ind Co Ltd Shanghai 201708 Peoples R China Hebei Prov Automot Safety Parts Technol Innovat Ct Zhuozhou 072750 Peoples R China Jiangsu Univ Sch Management 301 Xuefu Rd Zhenjiang 212013 Peoples R China

simultaneous localization and mapping (SLAM) is one of the key technologies for mobile robots to achieve autonomous driving, and the lidar SLAM algorithm is the mainstream research scheme. Firstly, this paper introduces the overall framework of lidar SLAM, elaborates on the functions of front-end scan matching, loop closure detection, back-end optimization, and map building module, and summarizes the algorithms used. Then, the classical representative SLAM algorithms are described and compared from three aspects: pure lidar SLAM algorithm, multi-sensor fusion SLAM algorithm, and deep learning lidar SLAM algorithm. Finally, the challenges faced by the lidar SLAM algorithm in practical use are discussed. The development trend of the lidar SLAM algorithm is prospected from five dimensions: lightweight, multi-sensor fusion, combination of new sensors, multi-robot collaboration, and deep learning. This paper can provide a brief guide for novices entering the field of SLAM and provide a comprehensive reference for experienced researchers and engineers to explore new research directions.

关键词： simultaneous localization and mapping lidar multi-sensor fusion deep learning development tendency

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Unlocking robotic perception: comparison of deep learning methods for simultaneous localization and mapping and visual simultaneous localization and mapping in robot

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2025年 1-33页

作者： Hoang, Minh Long Univ Parma Dept Engn & Architecture I-43124 Parma Italy

simultaneous localization and mapping (SLAM) and Visual SLAM are crucial technologies in robotics, allowing autonomous systems to navigate and comprehend their environment. Deep learning (DL) has become a powerful tool in driving progress in these areas, providing solutions that improve accuracy, efficiency, and resilience. This article thoroughly analyzes different deep learning techniques designed explicitly for SLAM and Visual SLAM applications in robotic systems. This work provides a detailed overview of DL roles in SLAM and VSLAM and emphasizes the differences between these two fields. Five powerful DL methods are investigated: Convolutional Neural Networks in extracting features and understanding meaning, Recurrent Neural Network in modeling temporal relationships, Deep Reinforcement Learning in developing exploration strategies, Graph Neural Network in modeling spatial relationships, and Attention Mechanisms in selectively processing information. In this research, we will examine the advantages and disadvantages of each approach in relation to robotic applications, taking into account issues such as real-time performance, resource restrictions, and adaptability to various situations. This article seeks to guide researchers and practitioners in selecting suitable deep learning algorithms to improve the capabilities of SLAM and Visual SLAM in robotic systems by combining ideas from recent research and actual implementations. The popular types of each concerned DL will be synthesized with the discussion of pros and cons.

关键词： Deep learning simultaneous localization and mapping Visual SLAM Robot

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Deep learning-based 6-DoF visual relocalization assisted simultaneous localization and mapping (SLAM)

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2025年

作者： Wang, Shuo Li, Xin Zhang, Yu Ma, Songhui Ren, Xianrui Changan Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China

PurposeVisual simultaneous localization and mapping (SLAM) methods suffer from accumulated errors, especially in challenging environments without loop closure. By constructing lightweight offline maps and using deep learning (DL)-based technology in the two stages, i.e. image retrieval and feature matching, the goal is to reconstruct the six-degree-of-freedom (6-DoF) relationship between SLAM sequences and map sequences. This study aims to propose a comprehensive coarse-to-fine 6-DoF long-term visual relocalization assisted SLAM method specifically designed for challenging environments, aiming to achieve more accurate pose ***/methodology/approachFirst, image global feature matching and patch-level global feature matching are conducted to achieve optimal frame-to-frame matching. Second, a DL network is introduced to extract and match features between the most similar frames, enabling point-to-point motion estimation. Finally, a fast pose graph optimization method is proposed to achieve real-time optimization of the pose in the SLAM *** proposed method has been successfully validated on the real-world FinnForest Dataset and UZH-FPV Drone Racing Dataset. The accuracy of the proposed method is evaluated using absolute positional error and absolute rotational error. Experimental results show that in most cases, there are significant improvements in the root mean square error and the standard deviation of the error in pose estimation, and it performs better than loop closure in terms of accuracy. This indicates that the method has strong generalizability and ***/valueThe main contribution of this study is the proposal of a complete DL-based coarse-to-fine 6-DoF long-term visual relocalization method to assist vSLAM, which demonstrates enhanced robustness and generalizability and can eliminate cumulative errors in pose estimation under challenging environments.

关键词： simultaneous localization and mapping Long-term map Semantic SLAM Visual relocalization

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A Massively Parallel Implementation of Gaussian Process Regression for Real Time Bathymetric Modeling and simultaneous localization and mapping

Field Robotics

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Field Robotics 2025年 2卷 940-970页

作者： Kristopher E. Krasnosky Christopher Roman Graduate School of Oceanography University of Rhode Island Narragansett RI 02882 University of Rhode Island Narragansett RI 02882

A Gaussian process regression (GPR) can be used as a stochastic method for modeling underwater terrain using multibeam sonar data. A GPR model can improve the effective resolution of a terrain model over traditional gridding methods and quantify uncertainty with an estimate of model variance over its entire domain. However, GPR solutions are extremely computationally expensive and generally reserved for post-processing applications. To make GPR viable for real-time applications, we developed massively parallel GPR (MP-GPR) to run on a graphical processing unit (GPU). MP-GPR is first used to process real-time multibeam data when assuming accurate navigation from a high precision position, heading, and attitude source. In underwater environments, however, we are denied the luxury of high precision position sensors and typically rely on dead reckoning. Therefore, MP-GPR was used as a terrain model for a featureless, Rao-Blackwellized particle filter based, bathymetric particle-filter simultaneous localization and mapping (BPSLAM) algorithm. Our GPU-based extension of BPSLAM (GP-BPSLAM) estimates many possible vehicle trajectories and MP-GPR predicts a possible map for each. By comparing the recent multibeam observations against the model for each possible trajectory, unlikely trajectories can be identified and removed. GP-BPSLAM is able to process data in real time and generate a navigation solution that is more accurate than simple dead reckoning.

关键词： simultaneous localization and mapping Computational modeling Real-time systems Navigation Sonar Uncertainty Trajectory Surveys Graphics processing units Gaussian processes

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Towards Collaborative simultaneous localization and mapping: A Survey of the Current Research Landscape

Field Robotics

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Field Robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative simultaneous localization and mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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