Robot Map Construction with LiDAR-Radar Reference Point Clouds
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Solution Overview
Problem
Current methods for constructing maps with mobile robots using direct scanning have deficiencies in accuracy and safety, particularly when obstacles occlude walls, leading to incomplete mapping and potential safety risks.
Innovation Solution
A method involving a radar probe arranged at a preset distance from a lidar center to determine reference point clouds, scan environments, and update virtual walls by fitting reference point clouds to overcome occlusions, ensuring complete mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct scanning method is used to construct environment maps, then the mapping process is simple and fast, but the mapping accuracy is low and completeness is poor when obstacles occlude walls
Solution Approach 1:
The mapping system is segmented into two independent components: a lidar for scanning visible surfaces and a radar probe for detecting occluded areas. The lidar generates initial point cloud data for directly visible walls, while the radar probe independently identifies occluded regions. This segmentation allows each component to specialize in its strength without compromising the other.
Solution Approach 2:
The radar probe acts as an intermediary device that bridges the gap between visible and occluded areas. It detects regions blocked from the lidar's view and provides supplementary data that fills mapping gaps. The radar probe mediates the information flow between the environment and the mapping system, ensuring complete wall detection even when occluded.
2Reliability
If direct scanning is used for wall detection, then the system is simple to operate, but safety risks increase when walls are occluded by obstacles
Solution Approach 1:
The detection function is segmented between lidar and radar probe. The lidar handles visible wall detection while the radar probe specifically targets occluded regions. This segmentation ensures that no wall segment is missed due to occlusion, improving reliability without requiring a completely redundant detection system.
Solution Approach 2:
The radar probe performs preliminary detection of potentially occluded areas before the lidar completes its scanning. By identifying occluded regions in advance, the system can allocate additional processing resources to these areas, ensuring complete wall detection and improving robot navigation safety.
3Productivity
If occluded walls are not completely mapped, then the mapping process is faster, but the robot requires re-scanning when obstacles move, reducing efficiency
Solution Approach 1:
The radar probe performs preliminary detection of occluded wall areas during the initial mapping process. By identifying and mapping these hidden regions upfront, the system creates a complete wall database that doesn't require updates when obstacles move, eliminating re-scanning needs and improving long-term productivity.
Solution Approach 2:
The system creates a complete virtual copy of all walls including occluded ones by combining lidar point cloud data with radar probe detection results. This comprehensive digital twin of the environment allows the robot to navigate accurately without physical re-scanning, saving time and improving efficiency.
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
Ensures comprehensive mapping by resolving occluded wall issues, improving robot safety and efficiency by eliminating the need for re-scanning when obstacles move, thus enhancing mapping accuracy and flexibility.
Implementation Method 1
controlling the lidar of the mapping robot to scan a target environment to obtain scanning data
Implementation Method 2
arranging a radar probe according to a preset distance by taking a center of a lidar of a mapping robot as a starting point, where the radar probe is configured to determine a reference point cloud
Data Source
AI summary
Disclosed are a map construction method, a robot, and a storage medium. A center of a lidar of a mapping robot is taken as a starting point, and a radar probe is arranged according to a preset distance; the radar probe is configured to determine a reference point cloud; the lidar of the mapping robot is controlled to scan the target environment to obtain scanning data; the target environment includes a wall and an obstacle; according to the scanning data, a virtual wall corresponding to the wall is determined; according to the scanning data, the reference point cloud determined by the radar probe is fitted to update the virtual wall to obtain an updated virtual wall; a target map including the updated virtual wall is generated.