Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Tixiao Shan"'
Publikováno v:
Current Robotics Reports. 2:177-188
The era of robotics-based environmental monitoring has given rise to many interesting areas of research. A key challenge is that robotic platforms and their operations are typically constrained in ways that limit their energy, time, or travel distanc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78ad8425e4ab43644c8eed9cc6ff4e1f
https://doi.org/10.1007/s43154-021-00045-6
https://doi.org/10.1007/s43154-021-00045-6
We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot's coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbf1a3b1f393d4b577cdac8ab7658f79
http://arxiv.org/abs/2202.08359
http://arxiv.org/abs/2202.08359
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783030954581
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4ea3a1b10f49a64251fff64054eaf87
https://doi.org/10.1007/978-3-030-95459-8_41
https://doi.org/10.1007/978-3-030-95459-8_41
Publikováno v:
IEEE Transactions on Robotics. 35:953-966
In this paper, we consider the problem of building descriptive three-dimensional (3-D) maps from sparse and noisy range sensor data. We expand our previously proposed method leveraging Bayesian kernel inference for prediction of occupancy in location
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e73bdacc6b972c2ad43d98d09dd3c021
https://doi.org/10.1109/tro.2019.2912487
https://doi.org/10.1109/tro.2019.2912487
Publikováno v:
ICRA
This paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b0967576b86e78501be1ac53694b8db
https://doi.org/10.1109/icra48506.2021.9561917
https://doi.org/10.1109/icra48506.2021.9561917
Publikováno v:
ICRA
We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d6c940df39df6d3992284d86a4697ed
http://arxiv.org/abs/2104.10831
http://arxiv.org/abs/2104.10831
Publikováno v:
ICRA
We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of an imaging lidar, we project the point cloud and obtain an intens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c03440b55922bb2992c8415e628f203
http://arxiv.org/abs/2103.02111
http://arxiv.org/abs/2103.02111
Publikováno v:
IROS
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a fac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cb9da2da239c118759f0d9b27a90da8
https://doi.org/10.1109/iros45743.2020.9341176
https://doi.org/10.1109/iros45743.2020.9341176
Publikováno v:
CDC
We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea042a5cdb6fb04878246ed3009697aa
http://arxiv.org/abs/2007.08362
http://arxiv.org/abs/2007.08362
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb012d26443e9bc44306444d7d8171c0
http://arxiv.org/abs/2004.05242
http://arxiv.org/abs/2004.05242