EIL‐SLAM: Depth‐enhanced edge‐based infrared‐LiDAR SLAM

Chen, Wenqiang; Wang, Yu; Chen, Haoyao; Liu, Yunhui

doi:10.1002/rob.22040

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…Additionally, the dataset captured objects from favorable viewpoints, with each entry containing image sequences, corresponding contours, and complete calibration parameters. For a more scientifically rigorous assessment of the proposed algorithm’s performance, the study conducted comparative experiments with mainstream SLAM algorithms, including Improved SLAM Based on LiDAR (LiDARO-SLAM) and SLAM Based on the Fusion of Binocular Wire Features and Inertial Navigation (BWFIN-SLAM) [ 29 , 30 ].…”

Section: Analysis Of Ims-vslam Algorithm For Wheeled Robotsmentioning

confidence: 99%

Vision SLAM algorithm for wheeled robots integrating multiple sensors

Zhou,

Zhou

2024

PLoS ONE

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Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios.

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Section: Analysis Of Ims-vslam Algorithm For Wheeled Robotsmentioning

confidence: 99%

Vision SLAM algorithm for wheeled robots integrating multiple sensors

Zhou,

Zhou

2024

PLoS ONE

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“…The significance of simultaneous localization and mapping (SLAM) technology and trajectory interpolation for mobile robots and autonomous driving has been increasing due to the continuous development of artificial intelligence technology [1][2][3][4][5][6]. SLAM algorithms and trajectory interpolation have been successfully applied in various fields, including campus inspection, logistics and distribution, and unmanned driving.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method is unsatisfactory as it does not take into account the semantic information of the surrounding environment, which is crucial for human beings to recognize whether a place has been reached or not [9][10]. Unfortunately, there The significance of simultaneous localization and mapping (SLAM) technology and trajectory interpolation for mobile robots and autonomous driving has been increasing due to the continuous development of artificial intelligence technology [1][2][3][4][5][6]. SLAM algorithms and trajectory interpolation have been successfully 2 applied in various fields, including campus inspection, logistics and distribution, and unmanned driving.…”

Section: Introductionmentioning

confidence: 99%

Research on a SLAM Method Based on Semantic Information

SUN,

ZHAO,

et al. 2024

Preprint

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This paper proposes a solution for 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. Secondly, the concept of voxel 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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“…HIBAS has important implications for fields that use 3D lidar data in real-time applications, such as robotics and autonomous driving, where the handling of large point clouds is a crucial task. In these fields, the use of advanced sensor technologies, such as lidar, has led to the generation of voluminous and multifaceted point clouds that cannot be applied directly to real-time perception problems (Nguyen et al, 2019), such as simultaneous localisation and mapping (SLAM) (Chen et al, 2022), 3D object reconstruction (Karami et al, 2022), 3D environment exploration (Butkiewicz et al, 2008), object detection (Burkert et al, 2011), classification (Mayr et al, 2017) and segmentation (Awwad et al, 2010). By reducing the size of the point cloud and simplifying the data, the proposed method can facilitate the development of real-time applications that require high accuracy and low computational cost (Han et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A histogram‐based sampling method for point cloud registration

Ervan

Temeltaş

2023

The Photogrammetric Record

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Accurate and efficient point cloud registration is essential in various fields, such as robotics, autonomous driving and medical imaging. The size of point clouds presents a significant challenge for existing registration methods. In this paper, a novel point cloud sampling method to improve the performance of the point cloud registration process is proposed. Instead of geometric feature preservation, which is preferred in most existing sampling methods, our approach scales every point and groups the scaled points into clusters to generate a histogram for the point cloud. The histogram is then used to identify the most significant regions of the point cloud to create the downsampled output data. Experimental results demonstrate that the proposed method improves accuracy and is robust against noise. Registration errors are reduced by up to 7% in rotation and 116% in translation. Additionally, the proposed method filtered out up to 98% of noise from the point cloud that was uniformly distributed at a rate of 25%.

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EIL‐SLAM: Depth‐enhanced edge‐based infrared‐LiDAR SLAM

Cited by 21 publications

References 33 publications

Vision SLAM algorithm for wheeled robots integrating multiple sensors

Vision SLAM algorithm for wheeled robots integrating multiple sensors

Research on a SLAM Method Based on Semantic Information

A histogram‐based sampling method for point cloud registration

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