Point Cloud Cluster Fusion for Timely Environmental Map Updates
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Solution Overview
Problem
Existing technologies struggle to efficiently update data representing a surrounding environment in response to changes, leading to inefficiencies in maintaining accurate environmental maps.
Innovation Solution
An information processing apparatus, method, and program that fuse first clusters from a recent point cloud data set with second clusters from an earlier data set to update the cluster groups, allowing for efficient compression and updating of environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is processed without clustering, then measurement precision is maintained, but data handling complexity and computational load increase significantly
Solution Approach 1:
The patent segments the point cloud data into multiple clusters based on spatial proximity and environmental features. Each cluster represents a grouped set of points that share similar characteristics, allowing the system to process representative samples from each cluster rather than every individual point, thereby reducing computational complexity while preserving measurement precision.
Solution Approach 2:
The patent transforms the raw point cloud data into clustered representations by changing the data structure parameters. Instead of handling individual coordinate points, the system operates on cluster centroids and aggregated features, effectively changing the parameter representation from point-level to cluster-level while maintaining the essential environmental information.
2Loss of time
If all point cloud data is processed in real-time, then environmental updates are timely, but computational load and processing time increase
Solution Approach 1:
The patent applies partial action by processing only the necessary subset of data - specifically, cluster representatives rather than all points. This allows the system to achieve timely environmental updates by focusing computational resources on key cluster information rather than exhaustively processing every point in the point cloud, thereby reducing power consumption while maintaining update timeliness.
3Measurement precision
If cluster fusion is performed between time series data, then environmental representation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges clusters from different time series by identifying corresponding clusters across temporal data and fusing their information. This merging process integrates environmental observations from multiple time points into unified cluster representations, improving the accuracy and robustness of environmental modeling while managing fusion complexity through systematic cluster matching and merging algorithms.
Data Source
AI summary
An information processing apparatus includes a fusion section that fuses each of first clusters included in a first cluster group with each of second clusters included in a second cluster group to update the second cluster group, the first cluster group into which a piece of first point cloud data obtained by a sensor is clustered, and the second cluster group generated on the basis of a piece of second point cloud data obtained earlier than the piece of first point cloud data.