ICRA 2011 Paper Abstract

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Paper TuA114.5

Paul, Rohan (University of Oxford), Newman, Paul (Oxford University)

Self Help: Seeking Out Perplexing Images for Ever Improving Navigation

Scheduled for presentation during the Regular Sessions "Visual Navigation I" (TuA114), Tuesday, May 10, 2011, 09:20−09:35, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Visual Navigation, Learning and Adaptive Systems, Mapping

Abstract

This paper is a demonstration of how a robot can, through introspection and then targeted data retrieval, improve its own performance. It is a step in the direction of lifelong learning and adaptation and is motivated by the desire to build robots that have plastic competencies which are not baked in. They should react to and benefit from use. We consider a particular instantiation of this problem in the context of place recognition. Based on a topic based probabilistic model of images, we use a measure of perplexity to evaluate how well a working set of background images explain the robot's online view of the world. Offline, the robot then searches an external resource to seek out additional background images that bolster its ability to localise in its environment when used next. In this way the robot adapts and improves performance through use.

 

 

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