Mowing Robot Operation Mapping with Point Cloud Obstacle Delineation
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Solution Overview
Problem
Current operation map construction methods for mowing robots are inefficient, requiring manual detection and data transmission for each new environment, leading to repetitive measurement and input processes.
Innovation Solution
An operation map construction method and apparatus that utilizes laser point cloud data to determine candidate obstacles, obtains feature images to identify a target obstacle, and delineates operational and non-operational areas in the target map, thereby improving efficiency through automated data acquisition and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection and data transmission is performed for each new environment, then the mowing robot can obtain operational area information, but the operation map construction efficiency is low and repetitive measurements are required
Solution Approach 1:
The patent applies preliminary action by pre-establishing a universal coordinate system and obstacle library that can be reused across different environments. The processing device pre-processes point cloud data to identify candidate obstacles and stores them in a standardized format, eliminating the need for repetitive manual measurement and data input for each new lawn environment.
Solution Approach 2:
The patent uses copying by creating a standardized operation map template with pre-defined coordinate systems and obstacle classifications. Once the operational area is delineated in one environment, the same map structure and coordinate system can be copied and adapted to other environments, reducing repetitive work while maintaining measurement precision.
2Productivity
If automated laser point cloud data acquisition is used, then the operation map construction efficiency is improved, but the complexity of data processing increases
Solution Approach 1:
The patent applies segmentation by dividing the complex data processing task into distinct modules: point cloud data acquisition, candidate obstacle determination, feature image extraction, and operational area delineation. Each module handles a specific aspect of the data, making the overall complex process manageable and systematic.
Solution Approach 2:
The processing device acts as an intermediary between the laser radar and the operation map generation system. It receives raw point cloud data, processes it through standardized algorithms to identify candidate obstacles, and outputs structured operational area information, thereby simplifying the interface between data acquisition and map construction.
3Speed
If candidate obstacles are determined from point cloud data without feature image verification, then the processing speed is faster, but the accuracy of obstacle identification decreases
Solution Approach 1:
The patent applies partial action by first quickly identifying candidate obstacles from point cloud data without full verification, then applying feature image analysis only to these candidates. This partial verification approach maintains high speed while improving accuracy for the most likely obstacle locations, avoiding the time cost of verifying every point cloud element.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enhances operation map construction efficiency by automating the detection and delineation of operational areas, reducing manual errors and the need for repetitive measurements, and enabling unified scanning of environments to determine obstacle positions quickly.
Implementation Method 1
acquiring laser point cloud data in an environment corresponding to a target map
Data Source
AI summary
An operation map construction method disclosed in embodiments of the present disclosure may include: acquiring laser point cloud data in an environment corresponding to a target map; determining candidate obstacles in the target map based on the laser point cloud data; obtaining feature images of the candidate obstacles and determining a target obstacle from the candidate obstacles based on the feature images; and delineating an operational area and a non-operational area in the target map based on the target obstacle.