Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Dames, Philip"'
Autor:
Xie, Zhanteng, Dames, Philip
This paper presents a 2D lidar semantic segmentation dataset to enhance the semantic scene understanding for mobile robots in different indoor robotics applications. While most existing lidar semantic datasets focus on 3D lidar sensors and autonomous
Externí odkaz:
http://arxiv.org/abs/2409.09899
Autor:
Srivastava, Alkesh K., Dames, Philip
In social robotics, a pivotal focus is enabling robots to engage with humans in a more natural and seamless manner. The emergence of advanced large language models (LLMs) such as Generative Pre-trained Transformers (GPTs) and autoregressive models li
Externí odkaz:
http://arxiv.org/abs/2407.09890
Autor:
Xie, Zhanteng, Dames, Philip
This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the
Externí odkaz:
http://arxiv.org/abs/2407.00144
The increased deployment of multi-robot systems (MRS) in various fields has led to the need for analysis of system-level performance. However, creating consistent metrics for MRS is challenging due to the wide range of system and environmental factor
Externí odkaz:
http://arxiv.org/abs/2405.01771
Autor:
Chen, Timothy, Shorinwa, Ola, Bruno, Joseph, Yu, Javier, Zeng, Weijia, Nagami, Keiko, Dames, Philip, Schwager, Mac
We present Splat-Nav, a real-time navigation pipeline designed to work with environment representations generated by Gaussian Splatting (GSplat), a popular emerging 3D scene representation from computer vision. Splat-Nav consists of two components: 1
Externí odkaz:
http://arxiv.org/abs/2403.02751
This paper presents a cooperative multi-robot multi-target tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy. The concept of normalized unused sensin
Externí odkaz:
http://arxiv.org/abs/2311.01707
Autor:
Xie, Zhanteng, Dames, Philip
Publikováno v:
IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2700-2719, 2023
This paper proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The poli
Externí odkaz:
http://arxiv.org/abs/2301.06512
Autor:
Xie, Zhanteng, Dames, Philip
This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a
Externí odkaz:
http://arxiv.org/abs/2210.08577
Autor:
Xiao, Xuesu, Xu, Zifan, Wang, Zizhao, Song, Yunlong, Warnell, Garrett, Stone, Peter, Zhang, Tingnan, Ravi, Shravan, Wang, Gary, Karnan, Haresh, Biswas, Joydeep, Mohammad, Nicholas, Bramblett, Lauren, Peddi, Rahul, Bezzo, Nicola, Xie, Zhanteng, Dames, Philip
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim of the challenge was to evaluate state-of-the-art autonomous ground na
Externí odkaz:
http://arxiv.org/abs/2208.10473
Autor:
Dames, Philip, Kumar, Vijay
This report outlines the procedure and results of an experiment to characterize a bearing-only sensor for use with PHD filter. The resulting detection, measurement, and clutter models are used for hardware and simulated experiments with a team of mob
Externí odkaz:
http://arxiv.org/abs/1502.04661