Related references
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Summary: Accurate maps are crucial for autonomous navigation robots, especially in handling large amounts of sensor data. Our proposed Voxfield framework can generate more accurate and complete maps online, with lower computational burden. Through a series of experiments, we demonstrate that our method outperforms existing techniques in map coverage, accuracy, and computational time.
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Summary: The study introduces a super-fast convergence approach for reconstructing the per-scene radiance field from a set of images with high quality in a short training time. This method outperforms traditional NeRF in terms of training time and quality.
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Summary: This paper presents NICE-SLAM, a dense SLAM system that incorporates multi-level local information through a hierarchical scene representation. It achieves detailed reconstruction on large indoor scenes and demonstrates competitive results in both mapping and tracking quality.
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Summary: The goal of this work is to reconstruct 3D models and synthesize novel views using data captured by scanning platforms, with significant improvements achieved through the utilization of lidar data, addressing exposure variation, and leveraging image segmentations. The system outperforms traditional methods and recent neural representations in Street View data.
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2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
Proceedings Paper
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Yiduo Wang et al.
Summary: The research presents an efficient, elastic 3D LiDAR reconstruction framework that addresses challenges in large-scale reconstruction. By introducing new submapping techniques and clustering fusion features, the system demonstrates improved scalability in large environments.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
(2021)
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Towaki Takikawa et al.
Summary: The study introduces an efficient neural representation that enables real-time rendering of high-fidelity neural SDFs and achieves state-of-the-art geometry reconstruction quality.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
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Yiyuan Pan et al.
Summary: The GEM system proposed in this article generates dense local and global elevation maps, maintains global consistency by updating relative poses between submaps, accelerates local mapping by integrating traversability analysis, and ensures real-time performance through CPU-GPU coordinated processing. Substantial experimental results validate the efficiency and effectiveness of GEM.
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2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
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IEEE ROBOTICS AND AUTOMATION LETTERS
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