Author name cluster

Rahul Biswas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAAI Conference 2005 Conference Paper

A Distributed Approach to Passive Localization for Sensor Networks

Rahul Biswas

Sensors that know their location, from microphones to vibration sensors, can support a wider arena of applications than their location unaware counterparts. We offer a method for sensors to determine their own location relative to one another by using only exogenous sounds and the differences in the arrivals of these sounds at different sensors. We present a distributed and computationally efficient solution that offers accuracy on par with more active and computationally intense methods.

PDF Details

IROS Conference 2004 Conference Paper

A passive approach to sensor network localization

Rahul Biswas
Sebastian Thrun

Sensor networks present the opportunity to accurately localize the phenomena of interest. To be able to do so however, sensor nodes, need themselves to be accurately localized. We present an algorithm to do this based on uncontrolled environmental sounds observed by each of the sensor nodes. A probabilistic generative model is presented and it is shown that the sensor node localization problem is equivalent to maximum likelihood estimation in the model. Experimental results are presented for both simulated sensor nodes and Crossbow MICA2 sensor nodes.

Details

UAI Conference 2002 Conference Paper

Learning Hierarchical Object Maps of Non-Stationary Environments with Mobile Robots

Dragomir Anguelov
Rahul Biswas
Daphne Koller
Benson Limketkai
Sebastian Thrun

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, recycling bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape parameters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot equipped with a laser range finder indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.

Details

IROS Conference 2002 Conference Paper

Towards object mapping in non-stationary environments with mobile robots

Rahul Biswas
Benson Limketkai
Scott Sanner
Sebastian Thrun

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time. Our approach uses a straightforward map differencing technique to detect changes in an environment over time. It employs 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. By combining data from multiple maps when learning object models, the resulting models have higher fidelity than could be obtained from any single map. A Bayesian complexity measure is applied to determine the number of different objects in the model, making it possible to apply the approach to situations where not all objects are present at all times in the map.

Details