Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Chaudhari, Pratik"'
We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing information-rich maps. Further, we develop a parallelized motion planning algorithm that can exploit the Gaussian map for real-time navigatio
Externí odkaz:
http://arxiv.org/abs/2409.18122
Autor:
Shao, Yifei Simon, Li, Tianyu, Keyvanian, Shafagh, Chaudhari, Pratik, Kumar, Vijay, Figueroa, Nadia
Constraint-aware estimation of human intent is essential for robots to physically collaborate and interact with humans. Further, to achieve fluid collaboration in dynamic tasks intent estimation should be achieved in real-time. In this paper, we pres
Externí odkaz:
http://arxiv.org/abs/2409.00215
Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains a significant challenge. Despite efforts to enhance model calibration,
Externí odkaz:
http://arxiv.org/abs/2408.08998
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak p
Externí odkaz:
http://arxiv.org/abs/2408.05839
Autor:
Ramesh, Rahul, Bisulco, Anthony, DiTullio, Ronald W., Wei, Linran, Balasubramanian, Vijay, Daniilidis, Kostas, Chaudhari, Pratik
We show that many perception tasks, from visual recognition, semantic segmentation, optical flow, depth estimation to vocalization discrimination, are highly redundant functions of their input data. Images or spectrograms, projected into different su
Externí odkaz:
http://arxiv.org/abs/2407.13841
Autor:
Chintapalli, Sai Spandana, Wang, Rongguang, Yang, Zhijian, Tassopoulou, Vasiliki, Yu, Fanyang, Bashyam, Vishnu, Erus, Guray, Chaudhari, Pratik, Shou, Haochang, Davatzikos, Christos
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data
Externí odkaz:
http://arxiv.org/abs/2407.12897
Autor:
Liu, Xu, Lei, Jiuzhou, Prabhu, Ankit, Tao, Yuezhan, Spasojevic, Igor, Chaudhari, Pratik, Atanasov, Nikolay, Kumar, Vijay
This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) approach that leverages a sparse and lightweight object-based representation to enable a heterogeneous robot team to autonomously explore 3D en
Externí odkaz:
http://arxiv.org/abs/2406.17249
We propose an online iterative algorithm to find a suitable convex cover to under-approximate the free space for autonomous navigation to delineate Safe Flight Corridors (SFC). The convex cover consists of a set of polytopes such that the union of th
Externí odkaz:
http://arxiv.org/abs/2406.09631
Autor:
Hsu, Christopher D., Chaudhari, Pratik
We study pursuit-evasion games in highly occluded urban environments, e.g. tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic targets on the ground. We show that we can build a neural radiance field (NeRF) representation of t
Externí odkaz:
http://arxiv.org/abs/2406.07431
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, DLIR methods forego many of the benefits of cla
Externí odkaz:
http://arxiv.org/abs/2406.07361