Zobrazeno 1 - 10
of 321
pro vyhledávání: '"Carlone, Luca"'
Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises the Sim2Real domain gap problem.
Externí odkaz:
http://arxiv.org/abs/2409.06240
Applications from manipulation to autonomous vehicles rely on robust and general object tracking to safely perform tasks in dynamic environments. We propose the first certifiably optimal category-level approach for simultaneous shape estimation and p
Externí odkaz:
http://arxiv.org/abs/2406.16837
Autor:
Hu, Siyi, Arroyo, Diego Martin, Debats, Stephanie, Manhardt, Fabian, Carlone, Luca, Tombari, Federico
Realistic conditional 3D scene synthesis significantly enhances and accelerates the creation of virtual environments, which can also provide extensive training data for computer vision and robotics research among other applications. Diffusion models
Externí odkaz:
http://arxiv.org/abs/2405.21066
Autor:
Zhang, Harry, Carlone, Luca
We introduce CHAMP, a novel method for learning sequence-to-sequence, multi-hypothesis 3D human poses from 2D keypoints by leveraging a conditional distribution with a diffusion model. To predict a single output 3D pose sequence, we generate and aggr
Externí odkaz:
http://arxiv.org/abs/2407.06141
We present a novel approach for long-term human trajectory prediction, which is essential for long-horizon robot planning in human-populated environments. State-of-the-art human trajectory prediction methods are limited by their focus on collision av
Externí odkaz:
http://arxiv.org/abs/2405.00552
Autor:
Maggio, Dominic, Chang, Yun, Hughes, Nathan, Trang, Matthew, Griffith, Dan, Dougherty, Carlyn, Cristofalo, Eric, Schmid, Lukas, Carlone, Luca
Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. While traditional closed-set metric-semantic maps were r
Externí odkaz:
http://arxiv.org/abs/2404.13696
Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale hybrid metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however how to
Externí odkaz:
http://arxiv.org/abs/2403.08094
This paper develops a new filtering approach for state estimation in polynomial systems corrupted by arbitrary noise, which commonly arise in robotics. We first consider a batch setup where we perform state estimation using all data collected from th
Externí odkaz:
http://arxiv.org/abs/2403.04712
Perceiving and understanding highly dynamic and changing environments is a crucial capability for robot autonomy. While large strides have been made towards developing dynamic SLAM approaches that estimate the robot pose accurately, a lesser emphasis
Externí odkaz:
http://arxiv.org/abs/2402.13817
We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set of objects at different poses (and possibly including points belongin
Externí odkaz:
http://arxiv.org/abs/2402.10865