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Qualitative map learning based on covisibility of objects.

Takehisa Yairi¹, Koichi Hori, Kosuke Hirama

¹Research Center For Advanced Science and Technology, University of Tokyo, Tokyo 153-8904, Japan. yairi@space.rcast.u-tokyo.ac.jp

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|September 1, 2005

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This study introduces a new method for mobile robots to build maps using qualitative data on object co-occurrence, eliminating the need for precise localization or quantitative measurements. This approach enables efficient spatial understanding for intelligent robot navigation.

Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Autonomous map construction is crucial for intelligent mobile robots.
Existing methods often rely on precise quantitative measurements and self-localization.
These requirements pose significant challenges in complex or unknown environments.

Purpose of the Study:

To propose a novel autonomous map construction method for mobile robots.
To utilize only qualitative information about simultaneous object observations.
To overcome limitations of traditional quantitative and self-localization-dependent approaches.

Main Methods:

Employing a heuristic that assumes closer objects are observed together more frequently.
Applying multidimensional scaling, a multivariate data analysis technique.

Related Experiment Videos

Processing qualitative data on the co-occurrence frequency of landmark objects.

Main Results:

Demonstrated a practical method for qualitative spatial relationship capture.
Successfully mapped environments without quantitative sensor data.
Showcased the ability to construct maps without robot self-localization information.

Conclusions:

The proposed method offers a viable alternative for autonomous map construction.
Qualitative co-occurrence data is sufficient for building spatial understanding.
This approach enhances the practicality of mobile robot mapping in diverse scenarios.