Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Sudeep Pillai"'
Autor:
Sudeep Pillai, Chao Fang, Kuan-Hui Lee, Adrien Gaidon, Jie Li, Matthew Kliemann, Wolfram Burgard
Publikováno v:
IROS
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b9f5d54fa86adc6796ba30879345836
https://doi.org/10.1109/iros45743.2020.9340931
https://doi.org/10.1109/iros45743.2020.9340931
Autor:
Wolfram Burgard, Vitor Guizilini, Greg Shakhnarovich, Sudeep Pillai, Rares Ambrus, Igor Vasiljevic, Adrien Gaidon
Publikováno v:
3DV
Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets. However, one significant limitation shared by current methods is the assumption of a known paramet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70496db2cc8588efa311de034ec9518e
Autor:
Vijay Gadepally, Michael Stonebraker, Aaron Zalewski, Samuel Madden, Sudeep Pillai, Oscar Moll
Publikováno v:
The Proceedings of the VLDB Endowment
State of the art sensors within a single autonomous vehicle (AV) can produce video and LIDAR data at rates greater than 30 GB/hour. Unsurprisingly, even small AV research teams can accumulate tens of terabytes of sensor data from multiple trips and m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::560e0baef340a7e7c382911a475d0589
https://doi.org/10.14778/3137765.3137822
https://doi.org/10.14778/3137765.3137822
Publikováno v:
CVPR
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular depth estimation method combining geometry with a new deep network, Pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::264ea9446bbda09b98c69b3abc57b6de
Publikováno v:
ICRA
Recent techniques in self-supervised monocular depth estimation are approaching the performance of supervised methods, but operate in low resolution only. We show that high resolution is key towards high-fidelity self-supervised monocular depth predi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad3435f58788f1522435de58071ddb01
Autor:
John J. Leonard, Sudeep Pillai
Publikováno v:
IROS
arXiv
arXiv
Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b99f90ffbbfd0481c17b453d6363f89
https://doi.org/10.1109/iros.2017.8206441
https://doi.org/10.1109/iros.2017.8206441
Publikováno v:
ICRA
Robotic systems typically require memory recall mechanisms for a variety of tasks including localization, mapping, planning, visualization etc. We argue for a novel memory recall framework that enables more complex inference schemas by separating the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dfe98d9bdb834b24e05a7b4194d1865
https://doi.org/10.1109/icra.2017.7989749
https://doi.org/10.1109/icra.2017.7989749
Publikováno v:
ICRA
arXiv
arXiv
Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faa929a033ca8e6f8f736b474074c21a
https://doi.org/10.1109/icra.2016.7487488
https://doi.org/10.1109/icra.2016.7487488
Autor:
Sudeep Pillai, John J. Leonard
Publikováno v:
Robotics: Science and Systems
In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorpora
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4156569da40ca35f5e3fbbc234fd1f5f
https://doi.org/10.15607/rss.2015.xi.034
https://doi.org/10.15607/rss.2015.xi.034
Publikováno v:
CVPR
We propose a simple and useful idea based on cross-ratio constraint for wide-baseline matching and 3D reconstruction. Most existing methods exploit feature points and planes from images. Lines have always been considered notorious for both matching a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b76ed21b21cb996dbe37552298708b9
https://doi.org/10.1109/cvpr.2015.7298728
https://doi.org/10.1109/cvpr.2015.7298728