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pro vyhledávání: '"Valada A"'
Navigating outdoor environments with visual Simultaneous Localization and Mapping (SLAM) systems poses significant challenges due to dynamic scenes, lighting variations, and seasonal changes, requiring robust solutions. While traditional SLAM methods
Externí odkaz:
http://arxiv.org/abs/2412.03263
Robust Simultaneous Localization and Mapping (SLAM) is a crucial enabler for autonomous navigation in natural, unstructured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasona
Externí odkaz:
http://arxiv.org/abs/2412.02506
Autor:
Kassab, Christina, Mattamala, Matías, Morin, Sacha, Büchner, Martin, Valada, Abhinav, Paull, Liam, Fallon, Maurice
3D open-vocabulary scene graph methods are a promising map representation for embodied agents, however many current approaches are computationally expensive. In this paper, we reexamine the critical design choices established in previous works to opt
Externí odkaz:
http://arxiv.org/abs/2412.01539
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sa
Externí odkaz:
http://arxiv.org/abs/2411.03408
Autor:
Kurenkov, Michael, Marvi, Sajad, Schmidt, Julian, Rist, Christoph B., Canevaro, Alessandro, Yu, Hang, Jordan, Julian, Schildbach, Georg, Valada, Abhinav
The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving prac
Externí odkaz:
http://arxiv.org/abs/2411.01909
Autor:
Irshad, Muhammad Zubair, Comi, Mauro, Lin, Yen-Chen, Heppert, Nick, Valada, Abhinav, Ambrus, Rares, Kira, Zsolt, Tremblay, Jonathan
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural F
Externí odkaz:
http://arxiv.org/abs/2410.20220
Publikováno v:
8th Annual Conference on Robot Learning, 2024
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perform sensor fusion in a single view by projecting features from both moda
Externí odkaz:
http://arxiv.org/abs/2410.07475
Autor:
Distelzweig, Aron, Look, Andreas, Kosman, Eitan, Janjoš, Faris, Wagner, Jörg, Valada, Abhinav
In autonomous driving, accurate motion prediction is essential for safe and efficient motion planning. To ensure safety, planners must rely on reliable uncertainty information about the predicted future behavior of surrounding agents, yet this aspect
Externí odkaz:
http://arxiv.org/abs/2410.01628
Demonstration data plays a key role in learning complex behaviors and training robotic foundation models. While effective control interfaces exist for static manipulators, data collection remains cumbersome and time intensive for mobile manipulators
Externí odkaz:
http://arxiv.org/abs/2409.15095
Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasting; however, they do not consider the geometry of the scene. In this w
Externí odkaz:
http://arxiv.org/abs/2409.12008