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pro vyhledávání: '"P Nazeri"'
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
Most traversability estimation techniques divide off-road terrain into traversable (e.g., pavement, gravel, and grass) and non-traversable (e.g., boulders, vegetation, and ditches) regions and then inform subsequent planners to produce trajectories o
Externí odkaz:
http://arxiv.org/abs/2409.17479
We present VertiEncoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiEncoder can handle four different downstream tasks, including forward kinodyna
Externí odkaz:
http://arxiv.org/abs/2409.11570
Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant varia
Externí odkaz:
http://arxiv.org/abs/2403.16419
Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which pri
Externí odkaz:
http://arxiv.org/abs/2403.08109
While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., SE(2), such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces
Externí odkaz:
http://arxiv.org/abs/2402.07065
Autor:
Antony, Jibinraj, Hegiste, Vinit, Nazeri, Ali, Tavakoli, Hooman, Walunj, Snehal, Plociennik, Christiane, Ruskowski, Martin
Object Detection (OD) has proven to be a significant computer vision method in extracting localized class information and has multiple applications in the industry. Although many of the state-of-the-art (SOTA) OD models perform well on medium and lar
Externí odkaz:
http://arxiv.org/abs/2401.12729
Autor:
Majidpour, Amir, Jameel, Samer Kais, Majidpour, Jafar, Bagheri, Houra, Rashid, Tarik A., Nazeri, Ahmadreza, Aleaba, Mahshid Moheb
Introduction: The auditory brainstem response (ABR) is measured to find the brainstem-level peripheral auditory nerve system integrity in children having normal hearing. The Auditory Evoked Potential (AEP) is generated using acoustic stimuli. Interpr
Externí odkaz:
http://arxiv.org/abs/2401.17317
Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task becomes mo
Externí odkaz:
http://arxiv.org/abs/2309.12568
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 10, Pp 4308-4322 (2024)
Abstract The speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also
Externí odkaz:
https://doaj.org/article/dad05f971a984758849fc696152308d8
