Heterogeneous sensor fusion framework for autonomous mobile robot obstacle avoidance
Author(s)
Zia, Ali
Gulrez, Tauseef
Chaudhry, Tayyab
Griffith University Author(s)
Year published
2010
Metadata
Show full item recordAbstract
An accurate model creation and path-planning of the environment has been found to be an important precondition for various robotics tasks such as search and rescue, manipulation and obstacle avoidance. Over the past few years, the topic of learning model representations has received considerable attention e.g. building city models, reconstruction of ancient art and building other environment models. Majority of the environmental modelling research relied upon single sensor like cameras or lasers, to acquire features data. In this paper we present an approach which fuses vision and range information together to learn an ...
View more >An accurate model creation and path-planning of the environment has been found to be an important precondition for various robotics tasks such as search and rescue, manipulation and obstacle avoidance. Over the past few years, the topic of learning model representations has received considerable attention e.g. building city models, reconstruction of ancient art and building other environment models. Majority of the environmental modelling research relied upon single sensor like cameras or lasers, to acquire features data. In this paper we present an approach which fuses vision and range information together to learn an accurate and better environment approximation.
View less >
View more >An accurate model creation and path-planning of the environment has been found to be an important precondition for various robotics tasks such as search and rescue, manipulation and obstacle avoidance. Over the past few years, the topic of learning model representations has received considerable attention e.g. building city models, reconstruction of ancient art and building other environment models. Majority of the environmental modelling research relied upon single sensor like cameras or lasers, to acquire features data. In this paper we present an approach which fuses vision and range information together to learn an accurate and better environment approximation.
View less >
Conference Title
2010 10th International Conference on Intelligent Systems Design and Applications