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pro vyhledávání: '"Ansari, Junaid Ahmed"'
This work proposes a novel approach to social robot navigation by learning to generate robot controls from a social motion latent space. By leveraging this social motion latent space, the proposed method achieves significant improvements in social na
Externí odkaz:
http://arxiv.org/abs/2310.07335
Autor:
Daga, Swapnil, Nair, Gokul B., Ramesh, Anirudha, Sajnani, Rahul, Ansari, Junaid Ahmed, Krishna, K. Madhava
In this paper, we present BirdSLAM, a novel simultaneous localization and mapping (SLAM) system for the challenging scenario of autonomous driving platforms equipped with only a monocular camera. BirdSLAM tackles challenges faced by other monocular S
Externí odkaz:
http://arxiv.org/abs/2011.07613
Publikováno v:
IROS 2020
We present a simple, fast, and light-weight RNN based framework for forecasting future locations of humans in first person monocular videos. The primary motivation for this work was to design a network which could accurately predict future trajectori
Externí odkaz:
http://arxiv.org/abs/2011.04943
Autor:
Nair, Gokul B., Daga, Swapnil, Sajnani, Rahul, Ramesh, Anirudha, Ansari, Junaid Ahmed, Jatavallabhula, Krishna Murthy, Krishna, K. Madhava
In this paper, we tackle the problem of multibody SLAM from a monocular camera. The term multibody, implies that we track the motion of the camera, as well as that of other dynamic participants in the scene. The quintessential challenge in dynamic sc
Externí odkaz:
http://arxiv.org/abs/2002.03528
Autor:
Srikanth, Shashank, Ansari, Junaid Ahmed, R, Karnik Ram, Sharma, Sarthak, J., Krishna Murthy, K, Madhava Krishna
In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representat
Externí odkaz:
http://arxiv.org/abs/1903.10641
Autor:
Ansari, Junaid Ahmed, Sharma, Sarthak, Majumdar, Anshuman, Murthy, J. Krishna, Krishna, K. Madhava
Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such d
Externí odkaz:
http://arxiv.org/abs/1803.02057
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose,
Externí odkaz:
http://arxiv.org/abs/1802.09298
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