Robot Grid Mapping with Occupancy and Height Updates
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Solution Overview
Problem
Conventional robot navigation systems fail to accurately construct grid maps due to blind spots caused by occlusions, leading to reduced scene restoration quality.
Innovation Solution
A method involving the construction of occupancy and height grid maps using point clouds, where occupancy grid maps include probability values and height grid maps include height values, which are combined to update a target grid map, thereby addressing occlusions and improving scene restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot constructs grid map using only point cloud data from current field of view, then the construction process is simple and fast, but blind spots occur due to occlusion and scene restoration accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by maintaining a target grid map that accumulates environmental information over time. Before finalizing the grid map, the system continuously updates the target grid map with new point cloud data from different field of views, ensuring that occluded obstacles are detected from multiple angles before the map construction is complete
Solution Approach 2:
The system transitions from two-dimensional grid map representation to three-dimensional spatial reasoning by incorporating height information and performing ray casting in 3D space. This dimensional enhancement allows the system to detect occluded obstacles by simulating sight lines from different heights and angles, resolving blind spots that exist in purely 2D representations
2Measurement precision
If the robot moves to multiple positions to eliminate blind spots, then scene restoration accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The system implements feedback by continuously comparing the current field of view data with the existing target grid map. The update module uses this feedback to determine which grids need revision, allowing the system to efficiently incorporate new information without requiring complete remapping. This feedback mechanism enables the robot to maintain accurate maps while minimizing redundant movements and processing time
Solution Approach 2:
Instead of requiring the robot to move to every possible position to map the entire environment, the system applies partial action by updating only the portions of the grid map that are affected by current observations. The target grid map is incrementally refined with partial updates based on current field of view, achieving complete scene coverage through selective rather than exhaustive exploration
Data Source
AI summary
In one aspect, a grid map construction method includes: acquiring a point cloud of a target scene in the current field of view; according to the point cloud in the current field of view, constructing an occupation grid map and a height grid map corresponding to each other in the current field of view; and then updating a target grid map of the target scene according to a point cloud occupation probability value of each grid in the occupation grid map and a point cloud height value of each grid in the height grid map in the current field of view.