Robot Navigation Map Generation From 3D Point Clouds
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Solution Overview
Problem
Existing map generation systems for autonomous robots are unable to accurately generate two-dimensional maps that include height information for all obstacles, limiting their ability to navigate safely and efficiently in complex environments.
Innovation Solution
A map generation system that utilizes a 3D point cloud map input device to extract ground and obstacle point cloud data, processes this data to increase density, and projects it onto a 2D plane, allowing for the creation of a 2D map that includes all relevant height information, thereby enabling accurate obstacle avoidance during autonomous movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If only 2D obstacle information from ultrasonic sensors is used for map generation, then the map generation process is simple, but the navigation accuracy and obstacle detection capability are insufficient
Solution Approach 1:
The patent transitions from 2D ultrasonic sensor data to 3D point cloud data from depth cameras, adding the height dimension to obstacle detection. This enables the system to capture comprehensive spatial information including obstacle height, while the 2D map generation process remains computationally efficient by projecting 3D data onto a 2D plane for navigation purposes.
2Measurement precision
If 3D obstacle information from depth cameras is used to generate maps with height information, then the obstacle detection accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the necessary 2D projection of 3D point cloud data for map generation, separating the height information extraction from the navigation map creation. This allows the system to utilize 3D depth camera data for accurate obstacle detection while maintaining a simplified 2D map structure for efficient path planning and navigation.
Solution Approach 2:
The system projects 3D point cloud data onto a 2D plane to generate navigation maps, reducing computational complexity while preserving essential spatial information. The height dimension is separately processed to identify obstacles, enabling efficient 2D map generation without losing critical 3D obstacle characteristics.
3Reliability
If comprehensive 3D point cloud data is processed to extract ground and obstacle information, then the navigation accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent segments the 3D point cloud data processing into distinct stages: ground point extraction, obstacle point extraction, and 2D map generation. This segmentation allows parallel processing of different data types and enables the system to handle comprehensive 3D data for accurate navigation while optimizing processing time through efficient data flow management.
Data Source
AI summary
A map generating system for autonomous movement of a robot includes a three-dimensional (3D) point cloud map input device, a 3D point cloud map regeneration device that extracts only 3D point cloud data corresponding to an arbitrary reference height from the 3D point cloud map to regenerate the 3D point cloud map, an unoccupied area generation device that extracts ground point cloud data from the 3D point cloud map to set an unoccupied area into which the robot is able to move, an occupied area generation device that extracts obstacle point cloud data from the 3D point cloud map to set an occupied area into which the robot is unable to move, and a two-dimensional (2D) map generation device that adds the unoccupied area and the occupied area to an area on the 3D point cloud map to generate a 2D map.