R
Rahul Biswas
Researcher at Stanford University
Publications - 9
Citations - 558
Rahul Biswas is an academic researcher from Stanford University. The author has contributed to research in topics: Mobile robot & Occupancy grid mapping. The author has an hindex of 8, co-authored 9 publications receiving 549 citations. Previous affiliations of Rahul Biswas include University of California, Berkeley.
Papers
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Journal ArticleDOI
ICEBERG: an Internet core network architecture for integrated communications
Helen J. Wang,Bhaskaran Raman,Chen-Nee Chuah,Rahul Biswas,Ramakrishna Gummadi,B. Hohlt,Xia Hong,Emre Kiciman,Zhuoqing Mao,Jimmy S. Shih,L. Subraimanian,B.Y. Zhno,Anthony D. Joseph,Randy H. Katz +13 more
TL;DR: The ICEBERG project at UC Berkeley is developing an Internet-based integration of telephony and data services spanning diverse access networks that leverages the Internet's low cost of entry for service creation, provision, deployment, and integration.
Proceedings ArticleDOI
Towards object mapping in non-stationary environments with mobile robots
TL;DR: An occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time using the expectation maximization algorithm to learn models of non-stationary objects and to determine the location of such objects in individual occupancy grid maps built at different points in time.
Proceedings Article
Learning hierarchical object maps of non-stationary environments with mobile robots
TL;DR: This paper presents an algorithm for learning object models of non-stationary objects found in office-type environments through a two-level hierarchical representation that outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.
Proceedings ArticleDOI
A passive approach to sensor network localization
Rahul Biswas,Sebastian Thrun +1 more
TL;DR: An algorithm to do this based on uncontrolled environmental sounds observed by each of the sensor nodes is presented and it is shown that the sensor node localization problem is equivalent to maximum likelihood estimation in the model.
Book ChapterDOI
Recognizing activities with multiple cues
TL;DR: The model is compact, requires only fifteen sentences of first-order logic grouped as a Dynamic Markov Logic Network (DMLNs) to implement the probabilistic model and leverages existing state-of-the-art work in pose detection and object recognition.