Autonomous learning of vision-based layered object models on mobile robots

Xiang Li, Mohan Sridharan, Shiqi Zhang
2011 2011 IEEE International Conference on Robotics and Automation  
Although mobile robots are increasingly being used in real-world applications, the ability to robustly sense and interact with the environment is still missing. A key requirement for the widespread deployment of mobile robots is the ability to operate autonomously by learning desired environmental models and revising the learned models in response to environmental changes. This paper presents an approach that enables a mobile robot to autonomously learn layered models for environmental objects
more » ... sing temporal, local and global visual cues. A temporal assessment of image gradient features is used to detect candidate objects, which are then modeled using color distribution statistics and a spatial representation of gradient features. The robot incrementally revises the learned models and uses them for object recognition and tracking based on a matching scheme comprising a spatial similarity measure and second order distribution statistics. All algorithms are implemented and tested on a wheeled robot platform in dynamic indoor environments.
doi:10.1109/icra.2011.5980435 dblp:conf/icra/LiSZ11 fatcat:s7wa52z52fccxcpjjuam6u6rre