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检索条件"主题词=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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Large-scale Indoor Mapping with Failure Detection and Recovery in slam

Large-scale Indoor Mapping with Failure Detection and Recove...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Rahman, Sharmin DiPietro, Robert Kedarisetti, Dharanish Kulathumani, Vinod Amazon Amazon Web Serv AWS Applicat Seattle MA 98109 USA

ISBN: (纸本)9798350377712;9798350377705

This paper addresses the failure detection and recovery problem in visual-inertial based Simultaneous Localization and Mapping (slam) systems for large-scale indoor environments. Camera and Inertial Measurement Unit (IMU) are popular choices for slam in many robotics tasks (e.g., navigation) due to their complementary sensing capabilities and low cost. However, vision has inherent challenges even in well-lit scenes, including motion blur, lack of features, or even accidental camera blockage. These failures can cause drifts to accumulate over time and can severely impact the scalability of existing solutions to large areas. To address these issues, we propose an automatic map generation service with (i) a failure detection method based on visual feature tracking quality using a health tracker which identifies and discards faulty measurements and (ii) a continuous session merging approach in slam. Taken together, this allows us to handle erroneous data without any manual intervention, and allows us to scale to extremely large spaces. The proposed system has been validated on benchmark datasets. Also, experimental results on multiple custom large-scale grocery stores, each between 1700 m(2) to 3700 m(2), and duration 60 to 80 minutes, are presented. Our approach shows the lowest error in all large-scale slam cases when compared with state-of-the-art visual-inertial slam packages, which often produce highly erroneous trajectories or lose track. Additionally, we provide dense 3D reconstruction with the presence of a depth camera by simply registering the point cloud from RGB-D image with respect to the slam generated trajectory - and the quality of the reconstruction illustrates the efficacy of our proposed method.

关键词： slam robotics

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

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

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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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Co-RaL: Complementary Radar-Leg Odometry with 4-DoF Optimization and Rolling Contact

Co-RaL: Complementary Radar-Leg Odometry with 4-DoF Optimiza...

引用

2024 International Conference on Intelligent Robots and Systems

作者： Jung, Sangwoo Yang, Wooseong Kim, Ayoung SNU Dept Mech Engn Seoul South Korea

ISBN: (纸本)9798350377712;9798350377705

Robust and accurate localization in challenging environments is becoming crucial for slam. In this paper, we propose a unique sensor configuration for precise and robust odometry by integrating chip radar and a legged robot. Specifically, we introduce a tightly coupled radar-leg odometry algorithm for complementary drift correction. Adopting the 4-DoF optimization and decoupled RANSAC to mmWave chip radar significantly enhances radar odometry beyond the existing method, especially z-directional even when using a single radar. For the leg odometry, we employ rolling contact modeling-aided forward kinematics, accommodating scenarios with the potential possibility of contact drift and radar failure. We evaluate our method by comparing it with other chip radar odometry algorithms using real-world datasets with diverse environments while the datasets will be released for the robotics community. https://***/SangwooJung98/Co-RaL-Dataset

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...

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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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From Underground Mines to Offices: A Versatile and Robust Framework for Range-Inertial slam 7

From Underground Mines to Offices: A Versatile and Robust Fr...

引用

7th Iberian robotics Conference

作者： Montano-Olivan, Lorenzo Placed, Julio A. Montano, Luis Lazaro, Maria T. Inst Tecnol Aragon ITA Zaragoza Spain Univ Zaragoza Inst Invest Ingn Aragon I3A Zaragoza Spain

ISBN: (纸本)9798350376371;9798350376364

Simultaneous Localization and Mapping (slam) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many slam systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-slam (LG-slam), a versatile range-inertial slam framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.

关键词： slam robotics

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STL-slam: A Structured-Constrained RGB-D slam Approach to Texture-Limited Environments

STL-SLAM: A Structured-Constrained RGB-D SLAM Approach to Te...

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2024 International Conference on Intelligent Robots and Systems

作者： Dong, Juan Lu, Maobin Chen, Chen Deng, Fang Chen, Jie Beijing Inst Technol Sch Automat Beijing Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing Peoples R China Beijing Inst Technol Beijing Peoples R China Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Most RGB-D-based slam methods assume texture-rich environments, making them susceptible to significant tracking errors or complete failures in the absence of texture features. Moreover, many existing methods encounter substantial rotation estimation errors, leading to long-term drift in tracking. This paper proposes a novel structured-constrained RGB-D slam method (STL-slam) for texture-limited environments. Compared to the existing methods, STL-slam can deal with environments without abundant texture information and significantly reduce long-term drift caused by rotation estimation errors. We assess the distribution complexity of pixels in an image by calculating the information entropy and pre-processing accordingly. We also present an efficient Manhattan Frames (MF) detection strategy based on orthogonal planes and lines. If MF is detected, we decouple rotation and translation, estimate drift-free rotation based on the Manhattan World (MW) coordinate system, and then estimate translation by minimizing the re-projection error of point, line, and plane features. In non-Manhattan Frames, the 6-DoF pose estimation is performed holistically, with the incorporation of structural constraints of parallel and perpendicular planes, as well as parallel and vertical lines, into the optimization process. Finally, we evaluate our method on public datasets and in real-world environments, which shows that our proposed method achieves superior performance compared to its counterparts.

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...

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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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