Shape information about the environment is accessible to a mobile equipped with a laser range finder and it provides robust and detailed information. We demonstrate that a spatial representation based on shape information is advantageous for mobile robots.

The goal of this project is to describe an object-centered, abstract spatial representation to be used as geometric foundation in mobile robot applications.

Characteristics of spatial representations currently used: 

Occupancy grids, bitmap like representations are widely used

• Not suitable for communication

• Requires additional sensor information in mapping applications (dead reckogning)

• Requires much data to be processed

Object maps based on simple geometric features (lines, special landmarks)

• Object-centered representation are judged necessary to cope with changing environments

• Presence of features limits applicability

• Feature extraction and recognition lacks of reliability

Geometric information remains largely uninterpreted.

Features of Shape

Shows of advantages from feature-based representations while avoiding its shortcomings:

• Provides an object-centered, abstract, and compact representation
• Representation grows by the environment's complexity, not by its size
• Object-centered representations are viewed a necessatity to tackle dynamic environments
• Is not limited to special working environments as shape is universal
• Algorithms can easily benefit from a more abstract representation resulting in better performance: less data needs to be processed and the data's structural organization can be exploited 

Bridges from metric to abstract information:

• Grants access to metric information, e.g., necessary for robot motion
• Offers a qualitative or topological view on configurations (of objects), enabling an interface to higher-level spatial reasoning

Applicable in robot mapping, localization, and navigation applications.

Key Techniques 

Extracting shape representation from perception

Shape-based tracking of objects

Updating the map & localizing within (SLAM)