Robust 3D Reconstruction Using HDR-Based SLAM

Yeh, Chia‐Hung; Lin, Min-Hui

doi:10.1109/access.2021.3051257

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…Moreover, the seamless integration of multi-sensor fusion techniques with SLAM not only bolsters the accuracy and robustness of 3D reconstruction, but also elevates the overall performance of SLAM systems by imparting richer and more intricate environmental information [44]. This groundbreaking advancement holds tremendous potential for a diverse array of practical applications, encompassing areas such as augmented and virtual reality, urban planning, and heritage preservation [45].…”

Section: Three-dimensional Reconstructionmentioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

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Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

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Section: Three-dimensional Reconstructionmentioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

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“…By adjusting the camera's exposure settings, the algorithm can capture images with optimal brightness levels, enhancing its performance under varying lighting conditions. High Dynamic Range (HDR) imaging [168] is another approach utilized to address lighting challenges. HDR imaging involves capturing multiple images taken at varying exposure levels and combining them to create a composite image with a broader range of luminance values.…”

Section: Handling Diverse Lighting Conditionsmentioning

confidence: 99%

A Survey of Visual SLAM Methods

Sahili,

Hassan,

Sakhrieh

et al. 2023

IEEE Access

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In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the past two decades as a powerful tool, empowering robots with the ability to navigate and map their surroundings. While these methods are traditionally confined to static environments, there has been a growing interest in developing V-SLAM to handle dynamic and realistic scenes. This survey offers a comprehensive overview of the current state-of-the-art V-SLAM methods, including their strengths and weaknesses. The paper also identifies the limitations of existing techniques and proposes potential research directions for future advancements. In addition, it provides an overview of commonly used datasets to evaluate the performance of V-SLAM methods. This survey sheds valuable insights into areas that need additional research to benefit V-SLAM development, including challenges related to limited scalability for systems with multiple agents, sensitivity to lighting changes, high computational cost, and performance issues in noisy environments.

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Adaptive fish school search optimized resnet for multi-view 3D objects reconstruction

Premalatha,

Parveen

2024

Multimed Tools Appl

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Robust 3D Reconstruction Using HDR-Based SLAM

Cited by 9 publications

References 34 publications

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

A Survey of Visual SLAM Methods

Adaptive fish school search optimized resnet for multi-view 3D objects reconstruction

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