Zobrazeno 1 - 10
of 1 857
pro vyhledávání: '"Kaess Michael"'
Autor:
Higuera, Carolina, Sharma, Akash, Bodduluri, Chaithanya Krishna, Fan, Taosha, Lancaster, Patrick, Kalakrishnan, Mrinal, Kaess, Michael, Boots, Byron, Lambeta, Mike, Wu, Tingfan, Mukadam, Mustafa
In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advances in robot manipulation as they markedly complement vision, yet soluti
Externí odkaz:
http://arxiv.org/abs/2410.24090
Autor:
Mick, Darwin, Pool, Taylor, Nagaraju, Madankumar Sathenahally, Kaess, Michael, Choset, Howie, Travers, Matt
We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point
Externí odkaz:
http://arxiv.org/abs/2410.08097
Autor:
Klammer, Christopher, Kaess, Michael
Ground to aerial matching is a crucial and challenging task in outdoor robotics, particularly when GPS is absent or unreliable. Structures like buildings or large dense forests create interference, requiring GNSS replacements for global positioning e
Externí odkaz:
http://arxiv.org/abs/2410.06410
Autor:
McGann, Daniel, Kaess, Michael
This paper introduces a novel incremental distributed back-end algorithm for Collaborative Simultaneous Localization and Mapping (C-SLAM). For real-world deployments, robotic teams require algorithms to compute a consistent state estimate accurately,
Externí odkaz:
http://arxiv.org/abs/2406.07371
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages \slices from high-dimensional surfaces to efficiently approximate posterior distributions of any shape. U
Externí odkaz:
http://arxiv.org/abs/2405.16453
We introduce BEVRender, a novel learning-based approach for the localization of ground vehicles in Global Navigation Satellite System (GNSS)-denied off-road scenarios. These environments are typically challenging for conventional vision-based state e
Externí odkaz:
http://arxiv.org/abs/2405.09001
Autor:
Qu, Ziyuan, Vengurlekar, Omkar, Qadri, Mohamad, Zhang, Kevin, Kaess, Michael, Metzler, Christopher, Jayasuriya, Suren, Pediredla, Adithya
Differentiable 3D-Gaussian splatting (GS) is emerging as a prominent technique in computer vision and graphics for reconstructing 3D scenes. GS represents a scene as a set of 3D Gaussians with varying opacities and employs a computationally efficient
Externí odkaz:
http://arxiv.org/abs/2404.04687
Autor:
Qadri, Mohamad, Zhang, Kevin, Hinduja, Akshay, Kaess, Michael, Pediredla, Adithya, Metzler, Christopher A.
Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring. Treacherous operating conditions, fragile surroundings, and limited navi
Externí odkaz:
http://arxiv.org/abs/2402.03309
Autor:
Suresh, Sudharshan, Qi, Haozhi, Wu, Tingfan, Fan, Taosha, Pineda, Luis, Lambeta, Mike, Malik, Jitendra, Kalakrishnan, Mrinal, Calandra, Roberto, Kaess, Michael, Ortiz, Joseph, Mukadam, Mustafa
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object's pose and shape. T
Externí odkaz:
http://arxiv.org/abs/2312.13469
State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios, the performance of these sensors is degraded by challenging environme
Externí odkaz:
http://arxiv.org/abs/2311.08608