Semantic Environment Mapping for Mobile Logistics Robots
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Solution Overview
Problem
Existing methods for producing environment maps for mobile logistics robots cannot identify the objects occupying cells, limiting the information content and functionality of these maps.
Innovation Solution
The method involves using a processor unit with an identification unit that includes object recognition and classification units to identify and categorize objects in the environment map, providing specific information on object location and type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional 2D or 3D sensor mapping methods are used to create environment maps, then the mapping process is simple and fast, but the information content is limited because objects occupying cells cannot be identified
Solution Approach 1:
The processor unit is divided into specialized functional modules: a 2D/3D mapping module for grid-based environment representation, and an object recognition module with classification units. This segmentation allows the system to perform both simple mapping and complex object identification simultaneously, resolving the contradiction between information content and processing complexity.
Solution Approach 2:
The system transitions from traditional 2D grid maps to enriched 3D semantic maps that include object classification data. By adding the dimension of semantic information (object types, properties) to the spatial grid structure, the system dramatically increases information content without fundamentally complicating the underlying mapping architecture.
2Loss of information
If object recognition and classification units are added to the processor unit, then semantic maps with higher information content can be produced, but the device complexity increases
Solution Approach 1:
The object recognition unit is designed to perform multiple functions: detecting objects in 2D maps, detecting objects in 3D maps, classifying objects by type, and determining object properties. This multi-functionality consolidates what could be separate complex systems into a single versatile module, reducing overall device complexity while maximizing information content.
Solution Approach 2:
The system uses intermediate data structures such as occupancy grids and object property databases that bridge the gap between raw sensor data and high-level semantic information. These intermediaries organize complex data in manageable formats, allowing the processor to handle rich information content without being overwhelmed by complexity.
3Measurement precision
If detailed object identification and classification are performed, then precise object localization and inventory tracking are enabled, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary 2D mapping and object detection before conducting detailed 3D classification. By first identifying object locations in the simpler 2D grid, then focusing computational resources only on classifying detected objects in 3D space, the system achieves high precision localization and classification while minimizing overall processing time through this staged approach.
Data Source
AI summary
A method for the production of an environment map (5) for a mobile logistics robot includes sensing an environment by use of a sensor system (2). The sensor data is evaluated in a processor unit (3), and a virtual grid of the environment is produced using cells. The cells in which objects (1) are detected are labeled as occupied cells and the cells in which no objects (1) are detected are labeled as free cells, as a result of which a representation of the environment is generated. The objects (1) that occupy the cells are identified in the processor unit (3). The mobile logistics robot carries out the method.