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检索条件"主题词=SLAM robotics"

共 184 条记录，以下是51-60 订阅

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MIMO Multipath-based slam for Non-Ideal Reflective Surfaces 27

MIMO Multipath-based SLAM for Non-Ideal Reflective Surfaces

引用

27th International Conference on Information Fusion (FUSION)

作者： Wielandner, Lukas Venus, Alexander Wilding, Thomas Witrisal, Klaus Leitinger, Erik Graz Univ Technol Graz Austria

ISBN: (纸本)9798350371420;9781737749769

Multipath-based simultaneous localization and mapping (MP-slam) is a well established approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future multiple input multiple output (MIMO) communication systems. Conventional methods for MP-slam consider specular reflections of the radio signals occurring at smooth, flat surfaces, which are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs), with each VA generating a single multipath component (MPC). However, non-ideal reflective surfaces (such as walls covered by shelves or cupboards) cause dispersion effects that violate the VA model and lead to multiple MPCs that are associated to a single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for MP-slam in MIMO communications systems. Our method considers non-ideal reflective surfaces by jointly estimating the parameters of individual dispersion models for each detected surface in delay and angle domain leveraging multiple-measurement-to-feature data association. We demonstrate that the proposed slam method can robustly and jointly estimate the positions and dispersion extents of ideal and non-ideal reflective surfaces using numerical simulation.

关键词： slam robotics

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BE-slam: BEV-Enhanced Dynamic Semantic slam with Static Object Reconstruction

BE-SLAM: BEV-Enhanced Dynamic Semantic SLAM with Static Obje...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Luo, Jun Wang, Gang Liu, Hongliang Wu, Lang Huang, Tao Xiao, Dengyu Pu, Huayan Luo, Jun Chongqing Univ Coll Mech & Vehicle Engn State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

The quality of a robot's environmental perception determines whether it can achieve more intelligent applications, such as semantic interaction with humans. slam, on the other hand, is one of the crucial capabilities for a robot to perceive its environment. However, when only a monocular image is provided, dynamic objects in the environment significantly impact the accuracy of map construction by the robot, leading to erroneous perception results. To address this issue, we propose a Visual slam framework based on BEV perception results, named BE-slam. With this framework, we can handle dynamic objects, occlusions, and incompletely observed objects. It can construct a stable static map by strengthening trust in static objects. Considering that object-level semantic maps can enhance a robot's perception abilities, we also reconstruct static objects in the map and use them to optimize the pose. Through experiments on existing publicly available dataset, we compare BE-slam with several existing methods that have shown good performance. The experimental results demonstrate that BE-slam performs exceptionally well on high-dynamic sequences and achieves comparable results on static or low-dynamic sequences.

关键词： slam robotics

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SMORE-slam: Semantic Monocular slam with Scale Correction and Reverse Loop Utilization in Outdoor Environments

SMORE-SLAM: Semantic Monocular SLAM with Scale Correction an...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Chen, Yushi Zhao, Fang Zhuge, Yue Liu, Junxiong Yan, Jiaquan Luo, Haiyong Beijing Univ Posts & Telecommun Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350377712;9798350377705

In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular slam methods, due to insufficient utilization of semantic information and neglect of leveraging reverse loop closures, result in significant scale drift and pose drift when confronted with such scenarios. In this paper, we introduce SMORE-slam, a semantic monocular slam with scale correction and reverse loop closure module. We constrain scale drift by harnessing semantic information across a wide spatial extent. Furthermore, we detect and correct reverse loop closures using semantic point cloud to reduce pose drift. Experimental results on the KITTI odometry dataset and the Oxford RobotCar dataset demonstrate the capability of our research in scale correction and reverse loop closure detection, enabling a reduction in trajectory errors of monocular slam.

关键词： slam robotics

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Active Semantic Mapping and Pose Graph Spectral Analysis for Robot Exploration

Active Semantic Mapping and Pose Graph Spectral Analysis for...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Zhang, Rongge Bong, Haechan Mark Beltrame, Giovanni Polytech Montreal Dept Comp Engn & Software Engn Montreal PQ Canada

ISBN: (纸本)9798350377712;9798350377705

Exploration in unknown and unstructured environments is a pivotal requirement for robotic applications. A robot's exploration behavior can be inherently affected by the performance of its Simultaneous Localization and Mapping (slam) subsystem, although slam and exploration are generally studied separately. In this paper, we formulate exploration as an active mapping problem and extend it with semantic information. We introduce a novel active metric-semantic slam approach, leveraging recent research advances in information theory and spectral graph theory: we combine semantic mutual information and the connectivity metrics of the underlying pose graph of the slam subsystem. We use the resulting utility function to evaluate different trajectories to select the most favorable strategy during exploration. Exploration and slam metrics are analyzed in experiments. Running our algorithm on the Habitat dataset, we show that, while maintaining efficiency close to the state-of-the-art exploration methods, our approach effectively increases the performance of metric-semantic slam with a 21% reduction in average map error and a 9% improvement in average semantic classification accuracy.

关键词： slam robotics

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A Robust Visual slam System for Small-Scale Quadruped Robots in Dynamic Environments

A Robust Visual SLAM System for Small-Scale Quadruped Robots...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Li, Chengyang Zhang, Yulai Yu, Zhiqiang Liu, Xinming Shi, Qing Beijing Inst Technol Intelligent Robot Inst Sch Mechatron Engn Beijing 100081 Peoples R China Beijing Inst Technol Sch Med Technol Jiazing 314000 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiazing 314000 Peoples R China Beijing Inst Technol Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

This paper presents a robust visual slam system designed for small-scale quadruped robots (ViQu-slam) for accurate localization, especially to mitigate the issue of erroneous data association caused by moving objects in dynamic environments. The proposed approach leverages a self-adaptive framework that integrates semantic segmentation with alterations in the spatial location of categorized map points. Besides, combination of leg odometry derived from forward kinematics with IMU provides scale information for positional transformations between keyframes, thus optimizing the overall localization accuracy of quadruped robots. At last, we performed evaluation across various stages and the results demonstrate competitive performance, with 53.16% reduction in average absolute trajectory error compared to that of ORB-slam3 in dynamic benchmark datasets. As a result, ViQu-slam, including visual and IMU-fused leg odometry, exhibits promising results on a small quadruped robot, reducing positioning errors in dynamic scenes by an average of 29.36% compared to existing state-of-the-art methods.

关键词： slam robotics

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HERO-slam: Hybrid Enhanced Robust Optimization of Neural slam

HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLA...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Xin, Zhe Yue, Yufeng Zhang, Liangjun Wu, Chenming Chinese Acad Sci Inst Automat Beijing Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China Baidu Res Robot & Autonomous Driving Lab RAL Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Simultaneous Localization and Mapping (slam) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit slam has shown encouraging and impressive results. However, the robustness of neural slam, particularly in challenging or data-limited situations, remains an unresolved issue. This paper presents HERO-slam, a Hybrid Enhanced Robust Optimization method for neural slam, which combines the benefits of neural implicit field and feature-metric optimization. This hybrid method optimizes a multi-resolution implicit field and enhances robustness in challenging environments with sudden viewpoint changes or sparse data collection. Our comprehensive experimental results on benchmarking datasets validate the effectiveness of our hybrid approach, demonstrating its superior performance over existing implicit field-based methods in challenging scenarios. HERO-slam provides a new pathway to enhance the stability, performance, and applicability of neural slam in real-world scenarios. Project page: https://***.

关键词： slam robotics

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A Point-to-distribution Degeneracy Detection Factor for LiDAR slam using Local Geometric Models

A Point-to-distribution Degeneracy Detection Factor for LiDA...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Ji, Sehua Chen, Weinan Su, Zerong Guan, Yisheng Li, Jiehao Zhang, Hong Zhu, Haifei Guangdong Univ Technol Sch Electromech Engineer Biomimet & Intelligent Robot Lab BIRL Guangzhou Guangdong Peoples R China Guangdong Acad Sci Inst Intelligent Mfg Guangdong Key Lab Modern Control Technol Guangzhou Guangdong Peoples R China JT Innovat Guangdong Intelligent Technol Co Ltd Guangzhou Peoples R China South China Agr Univ Coll Engn Guangzhou Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Limited by the working principles, LiDAR-slam systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-slam systems.

关键词： slam robotics

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Sharing Attention Mechanism in V-slam: Relative Pose Estimation with Messenger Tokens on Small Datasets

Sharing Attention Mechanism in V-SLAM: Relative Pose Estimat...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Dai, Dun Quan, Quan Cai, Kai-Yuan Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

In V-slam, the estimation of relative camera pose is crucial to determine the spatial relationship between consecutive camera images, helping to accurately track the movement of the camera in its environment. In small indoor scenes, when the training set is limited, which is very common in robot slam, learning-based methods may fail to converge, especially the Transformer architecture, which requires a more substantial dataset to match the performance of the CNN architecture model. This work addresses this problem with the sharing attention mechanism, building on recent improvements in solving visual Transformer architectures on small datasets while incorporating messenger tokens. Besides, double-embedding is introduced to capture the spatial of images and order of images. In summary, we introduce an intuitive end-to-end relative pose estimation solution and prove its accuracy on the two smallest sub-datasets of 7Scenes. The proposed method is tested with a set of comparison experiments conducted across CNN-based, Transformer-based end-to-end relative pose estimation models, and the robust feature-matching non-learning method. Our model outperforms in all comparisons. Furthermore, ablation studies clearly illustrate that these innovations are crucial for the accuracy of relative pose estimation on small datasets.

关键词： slam robotics

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RaNDT slam: Radar slam Based on Intensity-Augmented Normal Distributions Transform

RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal D...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Hilger, Maximilian Mandischer, Nils Corves, Burkhard Rhein Westfal TH Aachen Inst Mech Theory Machine Dynam & Robot Aachen Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Chair Percept Intelligent Syst Munich Germany Univ Augsburg Chair Mechatron Augsburg Germany

ISBN: (纸本)9798350377712;9798350377705

Rescue robotics sets high requirements to perception algorithms due to the unstructured and potentially vision-denied environments. Pivoting Frequency-Modulated Continuous Wave radars are an emerging sensing modality for slam in this kind of environment. However, the complex noise characteristics of radar slam makes, particularly indoor, applications computationally demanding and slow. In this work, we introduce a novel radar slam framework, RaNDT slam, that operates fast and generates accurate robot trajectories. The method is based on the Normal Distributions Transform augmented by radar intensity measures. Motion estimation is based on fusion of motion model, IMU data, and registration of the intensity-augmented Normal Distributions Transform. We evaluate RaNDT slam in a new benchmark dataset and the Oxford Radar RobotCar dataset. The new dataset contains indoor and outdoor environments besides multiple sensing modalities (LiDAR, radar, and IMU).

关键词： slam robotics

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CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms

CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation ...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Zhong, Shipeng Chen, Hongbo Qi, Yuhua Feng, Dapeng Chen, Zhiqiang Wu, Jin Wen, Weisong Liu, Ming Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative slam estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing slam tasks. In order to contribute to the research community, we will make our code open-source and accessible at https://***/PengYu-team/Co-LRIO

关键词： slam robotics

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