Map Segmentation for Motion Estimation in Dynamic Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vision-based motion estimation methods struggle with reduced accuracy in dynamic environments due to the presence of moving interfering objects, as they are designed under static environment assumptions.
Innovation Solution
A map segmentation method that separates static and dynamic points by removing edges with low inter-point correlation, using a graph-based approach to identify and eliminate the impact of dynamic points, allowing for accurate motion estimation in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision-based motion estimation methods are used with static environment assumptions, then the estimation method is simple and efficient, but the estimation accuracy deteriorates in dynamic environments due to moving interfering objects
Solution Approach 1:
The patent segments the map into static points and dynamic points by removing edges with low inter-point correlation. This segmentation allows the system to distinguish between static and dynamic elements, enabling accurate motion estimation using only static points while ignoring dynamic interfering objects.
Solution Approach 2:
The patent extracts and removes dynamic points from the map by identifying and removing edges with low inter-point correlation. This extraction process eliminates the harmful influence of moving objects from the motion estimation calculation, improving accuracy in dynamic environments.
2Measurement precision
If all map points are used for motion estimation, then the estimation process is simple, but the estimation accuracy is reduced due to the impact of dynamic points
Solution Approach 1:
The patent segments map points into static and dynamic categories through edge removal based on inter-point correlation. This segmentation enables the system to process only static points for motion estimation, improving accuracy while maintaining reasonable processing efficiency through the structured approach.
Solution Approach 2:
The patent extracts and removes dynamic points from the estimation process by eliminating edges with low inter-point correlation. This extraction ensures that only static points contribute to motion estimation, eliminating the accuracy-reducing impact of dynamic objects.
3Measurement precision
If edges with low inter-point correlation are removed to separate static and dynamic points, then the map segmentation improves motion estimation accuracy, but the graph structure becomes more complex
Solution Approach 1:
The patent applies segmentation by removing edges with low inter-point correlation to separate static and dynamic points. This creates a simplified graph structure that maintains only meaningful connections, improving motion estimation accuracy while managing graph complexity through systematic edge removal.
Solution Approach 2:
The patent changes the graph structure by modifying edge connections based on inter-point correlation thresholds. This parameter change approach allows the system to adapt the graph structure dynamically, improving accuracy while controlling complexity through threshold-based edge removal.
Data Source
AI summary
A map segmentation method and device, a motion estimation method, and a device terminal. Obtaining all map point data and constructing a corresponding graph based on these map points, where the graph includes each node formed by map points and corresponding edges; measuring and estimating to-be-estimated state variables corresponding to the node to obtain a corresponding measured and estimated value, where the to-be-estimated state variables include homogeneous coordinates of the node and a corresponding sensor pose upon node obtaining; calculating an inter-point correlation between any two adjacent nodes in the graph based on the measured and estimated value to determine whether the inter-point correlation is less than a preset correlation threshold; and when the inter-point correlation is less than the preset correlation threshold, removing an edge corresponding to the corresponding adjacent nodes, to separate the static points and the dynamic points and complete map segmentation.