Zobrazeno 1 - 10
of 213
pro vyhledávání: '"Osteen, Philip"'
Autor:
Thorne, David, Chan, Nathan, Ma, Yanlong, Robison, Christa S., Osteen, Philip R., Lopez, Brett T.
Keyframes are LiDAR scans saved for future reference in Simultaneous Localization And Mapping (SLAM), but despite their central importance most algorithms leave choices of which scans to save and how to use them to wasteful heuristics. This work prop
Externí odkaz:
http://arxiv.org/abs/2410.05576
Autor:
Dong, Zihao, Pflueger, Jeff, Jung, Leonard, Thorne, David, Osteen, Philip R., Robison, Christa S., Lopez, Brett T., Everett, Michael
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that either operate directly the full pointclouds or on extracted features fa
Externí odkaz:
http://arxiv.org/abs/2410.02961
M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions
Autor:
Datar, Aniket, Pokhrel, Anuj, Nazeri, Mohammad, Rao, Madhan B., Pan, Chenhui, Zhang, Yufan, Harrison, Andre, Wigness, Maggie, Osteen, Philip R., Ye, Jinwei, Xiao, Xuesu
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flig
Externí odkaz:
http://arxiv.org/abs/2410.01105
Autor:
Cai, Xiaoyi, Queeney, James, Xu, Tong, Datar, Aniket, Pan, Chenhui, Miller, Max, Flather, Ashton, Osteen, Philip R., Roy, Nicholas, Xiao, Xuesu, How, Jonathan P.
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to qua
Externí odkaz:
http://arxiv.org/abs/2409.03005
Autor:
Cai, Xiaoyi, Ancha, Siddharth, Sharma, Lakshay, Osteen, Philip R., Bucher, Bernadette, Phillips, Stephen, Wang, Jiuguang, Everett, Michael, Roy, Nicholas, How, Jonathan P.
Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision to automatically
Externí odkaz:
http://arxiv.org/abs/2311.06234
Autor:
Sharma, Lakshay, Everett, Michael, Lee, Donggun, Cai, Xiaoyi, Osteen, Philip, How, Jonathan P.
A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the p
Externí odkaz:
http://arxiv.org/abs/2210.06605
A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing work typically assumes no wheel slip or uses the expected traction for m
Externí odkaz:
http://arxiv.org/abs/2210.00153
Autor:
Schmeckpeper, Karl, Osteen, Philip R., Wang, Yufu, Pavlakos, Georgios, Chaney, Kenneth, Jordan, Wyatt, Zhou, Xiaowei, Derpanis, Konstantinos G., Daniilidis, Kostas
Publikováno v:
Field Robotics, 2, 147-171, 2022
This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior investig
Externí odkaz:
http://arxiv.org/abs/2204.05864
This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information. We leverage a neural information estimator to estimate the mutual information (MI) of semantic information
Externí odkaz:
http://arxiv.org/abs/2109.10270
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural
Externí odkaz:
http://arxiv.org/abs/2104.12023