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Algorithm for Semantic Labeling of Objects during Localization and Mapping in Robotic Vision

Description:

Brief Description: Probabilistic data association technique for labeling, mapping, and sensing objects during robotic traversal. 

 

Technology:

Simultaneous Localization and Mapping (SLAM) is a well-studied problem in robotics wherein a robot or autonomous vehicle uses its sensors to localize itself in relation to the environment, then updates a map of what it has seen as it traveled. These maps are typically point clouds that provide obstacle positions without the providing any context.

 

Penn professors Konstantinos Daniilidis and George Pappas have created a new algorithm that is more robust at recognizing new objects (such as a door or chair in an office setting) and classifying them correctly regardless of object orientation or sensor pose. This results in the first method to tightly couple inertial, geometric, and semantic observations into a single optimization framework. The method has been tested successfully in indoor and outdoor scenarios, demonstrating applicability for autonomous vehicle or home and warehouse robot applications.

 

 

Advantages:

•       Low computational cost

•       No expensive hardware required

•       Real-time SLAM and object recognition

 

Stage of Development:

•       Prototype and software developed for integration with robotic platforms

 

Intellectual Property:

Provisional Pending

 

Reference Media:

 

 

Desired Partnerships:

1.       License

2.       Co-development


Patent Information:
For Information, Contact:
Joshua Jeanson
Associate Director, SEAS/SAS Licensing Group
University of Pennsylvania
jeanson@upenn.edu
Inventors:
Konstantinos Daniilidis
George Pappas
Keywords: