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Physical Sciences and Mathematics Commons

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Artificial Intelligence and Robotics

University of Massachusetts Amherst

Computer Science Department Faculty Publication Series

Robotics

Publication Year

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Episodic Non-Markov Localization: Reasoning About Short-Term And Long-Term Features, Joydeep Biswas, Manuela M. Veloso Jan 2014

Episodic Non-Markov Localization: Reasoning About Short-Term And Long-Term Features, Joydeep Biswas, Manuela M. Veloso

Computer Science Department Faculty Publication Series

Markov localization and its variants are widely used for localization of mobile robots. These methods assume Markov independence of observations, implying that observations made by a robot correspond to a static map. However, in real human environments, observations include occlusions due to unmapped objects like chairs and tables, and dynamic objects like humans. We introduce an episodic non-Markov localization algorithm that maintains estimates of the belief over the trajectory of the robot while explicitly reasoning about observations and their correlations arising from unmapped static objects, moving objects, as well as objects from the static map. Observations are classified as arising …


Corrective Gradient Refinement For Mobile Robot Localization, Joydeep Biswas, Manuela M. Veloso, Brian Coltin Jan 2011

Corrective Gradient Refinement For Mobile Robot Localization, Joydeep Biswas, Manuela M. Veloso, Brian Coltin

Computer Science Department Faculty Publication Series

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then …


Wifi Localization And Navigation For Autonomous Indoor Mobile Robots, Joydeep Biswas, Manuela M. Veloso Jan 2010

Wifi Localization And Navigation For Autonomous Indoor Mobile Robots, Joydeep Biswas, Manuela M. Veloso

Computer Science Department Faculty Publication Series

Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We …