Zobrazeno 1 - 10
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pro vyhledávání: '"Rosinol, Antoni"'
Autor:
Rosinol, Antoni
3D Spatial Perception is the ability of an agent to perceive and understand the three-dimensional structure of its environment, including its position and orientation within that environment. This ability is essential for autonomous robots to navigat
Externí odkaz:
https://hdl.handle.net/1721.1/150288
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural
Externí odkaz:
http://arxiv.org/abs/2210.13641
We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation. The proposed approach is able to 3D reconstruct scenes densely, accurately, and in real-time while being robust t
Externí odkaz:
http://arxiv.org/abs/2210.01276
Autor:
Chang, Yun, Ebadi, Kamak, Denniston, Christopher E., Ginting, Muhammad Fadhil, Rosinol, Antoni, Reinke, Andrzej, Palieri, Matteo, Shi, Jingnan, Chatterjee, Arghya, Morrell, Benjamin, Agha-mohammadi, Ali-akbar, Carlone, Luca
Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degr
Externí odkaz:
http://arxiv.org/abs/2205.13135
Autor:
Rosinol, Antoni, Carlone, Luca
Meshes are commonly used as 3D maps since they encode the topology of the scene while being lightweight. Unfortunately, 3D meshes are mathematically difficult to handle directly because of their combinatorial and discrete nature. Therefore, most appr
Externí odkaz:
http://arxiv.org/abs/2108.02957
Autor:
Rosinol, Antoni, Violette, Andrew, Abate, Marcus, Hughes, Nathan, Chang, Yun, Shi, Jingnan, Gupta, Arjun, Carlone, Luca
Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), in
Externí odkaz:
http://arxiv.org/abs/2101.06894
Publikováno v:
34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes. These methods, however, e
Externí odkaz:
http://arxiv.org/abs/2010.12455
We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. Scene graphs are directed graphs where nodes represent entities in the scene (e.g. objects, walls, rooms), and edges represent relations (e.g. inclusion,
Externí odkaz:
http://arxiv.org/abs/2002.06289
We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM, VINS- Mono, OKVIS, ROVIO) by
Externí odkaz:
http://arxiv.org/abs/1910.02490
Publikováno v:
IEEE Int. Conf. Robot. Autom. (ICRA), 2019
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud representation of the scene that does not model the topology of the environment. A 3D mesh instead offers a richer, yet lightweight, model. Nevertheless, building a 3D mesh out
Externí odkaz:
http://arxiv.org/abs/1903.01067