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arXiv 2024年

作者： Schmidt, Fabian Blessing, Constantin Enzweiler, Markus Valada, Abhinav Institute for Intelligent Systems Esslingen University of Applied Sciences Esslingen Germany Department of Computer Science University of Freiburg Freiburg Germany

Simultaneous Localization and Mapping (slam) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. In agricultural applications, where environmental conditions can be particularly challenging due to variable lighting or weather conditions, Visual‐Inertial slam has emerged as a potential solution. This paper benchmarks several open‐source Visual‐Inertial slam systems, including ORB‐slam3, VINS‐Fusion, OpenVINS, Kimera, and SVO Pro, to evaluate their performance in agricultural settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems’ effectiveness in real-world environments and especially their application to embedded systems in agricultural robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual‐Inertial slam systems for practical outdoor applications in mobile robotics. Copyright © 2024, The Authors. All rights reserved.

关键词： slam robotics

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GeoFlow-slam: A Robust Tightly-Coupled RGBD-Inertial Fusion slam for Dynamic Legged robotics

arXiv

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arXiv 2025年

作者： Xiao, Tingyang Zhou, Xiaolin Liu, Liu Sui, Wei Feng, Wei Qiu, Jiaxiong Wang, Xinjie Su, Zhizhong Horizon Robotics Beijing China D-Robotics Beijing China

This paper presents GeoFlow-slam, a robust and effective Tightly-Coupled RGBD-inertial slam for legged robots operating in highly dynamic environments. By integrating geometric consistency, legged odometry constraints, and dual-stream optical flow (GeoFlow), our method addresses three critical challenges: feature matching and pose initialization failures during fast locomotion and visual feature scarcity in texture-less scenes. Specifically, in rapid motion scenarios ，feature matching is notably enhanced by leveraging dual-stream optical flow, which combines prior map points and poses. Additionally, we propose a robust pose initialization method for fast locomotion and IMU error in legged robots, integrating IMU/Legged odometry, inter-frame Perspective-n-Point (PnP), and Generalized Iterative Closest Point (GICP). Furthermore, a novel optimization framework that tightly couples depth-to-map and GICP geometric constraints is first introduced to improve the robustness and accuracy in long-duration, visually texture-less environments. The proposed algorithms achieve state-of-the-art (SOTA) on collected legged robots and open-source datasets. To further promote research and development, the open-source datasets and code will be made publicly available at https://***/NSN-Hello/GeoFlow-slam. Copyright © 2025, The Authors. All rights reserved.

关键词： slam robotics

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Robust Real-Time Localization System via Semantic Dimensional Chains for Degraded Scenarios

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IEEE Internet of Things Journal 2025年第9期12卷 12579-12588页

作者： Li, Yunfei Jiang, Lin Lei, Bin Tang, Bo Zhu, Jianyang Wuhan University of Science and Technology School of Mechanical Engineering and Automation Wuhan430081 China

To overcome the limitations of current visual and laser slam methods in narrow, feature-repetitive, and lighting-variable degraded scenarios, we propose a robust real-time mapping and localization system utilizing semantic dimensional chains (SDCs). SDC is a novel ordered semantic instance map representation. During the mapping phase, our system efficiently integrates diverse object detection and 2D-slam algorithms, constructing a prior semantic map through Bayesian filtering. In the preprocessing phase, a semantic map optimization algorithm based on neighborhood homogeneity is applied to effectively eliminate semantic noise. For localization, SDCs combined with precise localization algorithms enable rapid global localization. The system also features a kidnapping detection mechanism for swift pose recovery. We rigorously evaluated the system’s performance in terms of robustness to lighting interference, global localization, and pose recovery in real-world degraded environments. Experimental results indicate that our localization system outperforms existing methods. Compared to a state-of-the-art vision-based localization algorithm, our semantic matching (SM) module exhibits superior resistance to lighting interference, resulting in a 15.38% increase in localization success rate (SR) and a 27.71% reduction in average localization time. © 2014 IEEE.

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Adaptive-LIO: Enhancing Robustness and Precision Through Environmental Adaptation in LiDAR Inertial Odometry

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IEEE Internet of Things Journal 2025年第9期12卷 12123-12136页

作者： Zhao, Chengwei Hu, Kun Xu, Jie Zhao, Lijun Han, Baiwen Wu, Kaidi Tian, Maoshan Yuan, Shenghai Harbin Institute of Technology State Key Laboratory of Robotics and Systems Heilongjiang Harbin150001 China Hangzhou Qisheng Intelligent Techology Company Ltd. Zhejiang Hangzhou311217 China China University of Mining and Technology School of Mechatronic Engineering Jiangsu Xuzhou221116 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West 639798 Singapore University of Electronic Science and Technology School of Mechanical and Electrical Engineering Sichuan Chengdu611731 China

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, LiDAR inertial odometry (LIO) becomes increasingly prevalent in robotics and autonomous driving. However, many current slam systems lack sufficient adaptability to various scenarios. Challenges include decreased point cloud accuracy with longer frame intervals under the constant velocity assumption, coupling of erroneous IMU information when IMU saturation occurs, and decreased localization accuracy due to the use of fixed-resolution maps during indoor-outdoor scene transitions. To address these issues, we propose a loosely coupled adaptive LIO named Adaptive-LIO, which incorporates adaptive segmentation to enhance mapping accuracy, adapts motion modality through IMU saturation and fault detection, and adjusts map resolution adaptively using multiresolution voxel maps based on the distance from the LiDAR center. Our proposed method has been tested in various challenging scenarios, demonstrating the effectiveness of the improvements we introduce. The code is open-source on GitHub: Adaptive-LIO. © 2014 IEEE.

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General Place Recognition Survey: Towards Real-World Autonomy

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IEEE Transactions on robotics 2025年 41卷 3019-3038页

作者： Yin, Peng Jiao, Jianhao Zhao, Shiqi Xu, Lingyun Huang, Guoquan Choset, Howie Scherer, Sebastian Han, Jianda City University of Hong Kong Department of Mechanical Engineering 518057 Hong Kong University College London Department of Computer Science LondonWC1E 6BT United Kingdom University of Delaware Robot Perception and Navigation Group NewarkDE19716 United States Carnegie Mellon University Robotics Institute PittsburghPA15213 United States Nankai University Tianjin300071 China

In the realm of robotics, the quest for achieving realworld autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology. Despite the PR community's remarkable strides over the past two decades, garnering attention from fields like computer vision and robotics, the development of PR methods that sufficiently support real-world robotic systems remains a challenge. This paper aims to bridge this gap by highlighting the crucial role of PR within the framework of Simultaneous Localization and Mapping (slam) 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence (AI) technologies. For this goal, we provide a comprehensive review of the current state-of-the-art (SOTA) advancements in PR, alongside the remaining challenges, and underscore its broad applications in robotics. This paper begins with an exploration of PR's formulation and key research challenges. We extensively review literature, focusing on related methods on place representation and solutions to various PR challenges. Applications showcasing PR's potential in robotics, key PR datasets, and open-source libraries are discussed. We conclude with a discussion on PR's future directions and provide a summary of the literature covered at: https://***/Metaslam/GPRS. © 2004-2012 IEEE.

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Resource Utilization of 2D slam Algorithms in ROS-Based Systems: an Empirical Evaluation

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Journal of the Brazilian Computer Society 2025年第1期31卷 229-261页

作者： Hamer, Engel Albonico, Michel Malavolta, Ivano Vrije Universiteit Amsterdam Netherlands Federal University of Technology Netherlands

Simultaneous localization and mapping (slam) is an important task in robotic systems, which entails mapping an environment while keeping track of the robot’s position within the created map. The Robot Operating System (ROS) offers various packages for this functionality, where each one may lead to different performance and resource usage. Therefore, this study aims to investigate the impact of different ROS-based slam algorithms on resource utilization, including possible trade-offs with performance (e.g., the accuracy of the created map). The investigation is centered on primary experiments involving multiple runs of a single robot, which alternates between four slam algorithms: Cartographer, Gmapping, Hector slam, and Karto. During these experiments, the robot autonomously navigates through two types of arenas: point-to-point (multi-goal navigation) and circular (returning to the starting position after following the perimeter). Throughout these trials, the robot’s performance is assessed based on the ROS system’s efficiency and energy consumption. In a secondary set of experiments, the tests are repeated, but with key slam algorithm parameters reconfigured to evaluate their impact. The experiment results reveal Karto as the most efficient algorithm across all evaluated metrics, and the one that creates the most visually consistent maps. Cartographer was the algorithm that showed the least promising results regarding both, energy consumption and CPU utilization. Furthermore, Gmapping was the algorithm most susceptible to changes in slam algorithms’ parameter values. The results presented in this study are, therefore, key for resource-aware design choices when using slam algorithms in the context of ROS-based systems. © 2025, Brazilian Computing Society. All rights reserved.

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SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware slam 25

SuperNoVA: Algorithm-Hardware Co-Design for Resource-Aware S...

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30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Kim, Seah Hsiao, Roger Nikolic, Borivoje Demmel, James Shao, Yakun Sophia University of California Berkeley BerkeleyCA United States

ISBN: (纸本)9798400706981

Simultaneous Localization and Mapping (slam) plays a crucial role in robotics, autonomous systems, and augmented and virtual reality (AR/VR) applications by enabling devices to understand and map unknown environments. However, deploying slam in AR/VR applications poses significant challenges, including the demand for high accuracy, real-time processing, and efficient resource utilization, especially on compact and lightweight devices. To address these challenges, we propose SuperNoVA, which enables high-accuracy, real-time, large-scale slam in resource-constrained settings through a full-stack system, spanning from algorithm to hardware. In particular, SuperNoVA dynamically constructs a subgraph to meet the latency target while preserving accuracy, virtualizes hardware resources for efficient graph processing, and implements a novel hardware architecture to accelerate the slam backend efficiently. Evaluation results demonstrate that, for a large-scale AR dataset, SuperNoVA reduces full slam backend computation latency by 89.5% compared to the baseline out-of-order CPU and 78.6% compared to the baseline embedded GPU, and reduces the maximum pose error by 89% over existing slam solutions, while always meeting the latency target. © 2025 ACM.

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Deep Neural Network Based Relocalization of Mobile Robot in Visual slam 6th

Deep Neural Network Based Relocalization of Mobile Robot in ...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Hamza, Azhar Muhammad Shi, Chaoxia Wang, Yanqing Nanjing University of Science and Technology Nanjing210094 China Nanjing Xiaozhuang University Nanjing211171 China

ISBN: (纸本)9783031712524

Relocalization after track loss in Visual Simultaneous Localization and Mapping (Visual slam) is a critical challenge in robotics, especially for autonomous mobile robots navigating dynamic environments. This paper introduces a deep learning approach that employs Deep Neural Networks (DNNs), particularly VGG16 and ResNet34, to reorient and relocalize robots effectively. Trained on a vast repository of indoor images from the Multimodal Indoor Simulator (MINOS) and Matterport3D dataset, the DNN models discern the most viable direction for movement be it translation or rotation based on the robot’s current visual input in relation to its last known position within the ORB-slam2 generated map. The methodology involves real-time data exchange between MINOS and the ORB-slam2 system via a dedicated ROS node, facilitating the recovery process. Extensive testing shows that our proposed model successfully predicts the appropriate recovery action in over 90% of track loss instances, substantiating its efficacy and potential for deployment in real-world applications. This research contributes to the advancement of robust relocalization strategies in Visual slam, enhancing the autonomous capabilities of mobile robotics. © IFIP International Federation for Information Processing 2025.

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Improved Computation Efficiency 2D Visual slam Based on Particle Filter With Distance Sliding Window 25th

Improved Computation Efficiency 2D Visual SLAM Based on Par...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Zhang, Zhixin Liang, Yichen Stancu, Alexandru Department of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9783031720581

Simultaneous localization and mapping (slam) is one of the fundamental challenges for motion robot systems. Over time, slam methodologies have evolved from conventional filter-based approaches to contemporary optimization approaches. slam based on the particle filter has been widely used in mobile robot technology, especially in 2D ground robot slam. However, traditional implementation of particle filter visual slam algorithm typically only considers 2D position states, overlooking the orientation along the z-axis. Moreover, they entail substantial computational overhead as the state needs to be updated once there is a new visual observation received. To address these limitations, this paper presents a novel 2D visual slam framework that incorporates three freedoms and leverages Aruco Marker as the robust observation. A distance sliding window is introduced to avoid the computational workload resulting from the expansion of state dimensionality. Several simulation experimental results demonstrate that the proposed algorithm significantly enhances computational efficiency while maintaining accuracy and robustness in both localization and mapping tasks, through a reduced requirement for visual updates. The whole implementation is open source (https://***/Happy-ZZX/Puzzlebot). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Keyframe Selection Strategy for Visual slam based on Fuzzy Analysis 24

Keyframe Selection Strategy for Visual SLAM based on Fuzzy A...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Peng, Yilong Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798400710865

Keyframe Selection is essential in Visual slam systems. In complex environments, existing keyframe selection strategies often generate robot trajectories with significant errors due to unsatisfactory alignment with actual scenes. This paper proposes a keyframe selection strategy for visual slam, modeling robots’ motion trajectory parameters using fuzzy sets. After representing each uncertain trajectory characteristic parameter by a fuzzy function, we build a fuzzy implication reasoning model for filtering frames with distinct trajectory characteristics, achieving real-time selection of keyframes. In the experiments, we integrate the proposed selection strategy with mainstream monocular visual slam algorithms and validate the effectiveness of our strategy using the KITTI dataset. © 2024 Copyright held by the owner/author(s).

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