3D Box Modeling for Precise Robot Picking Alignment

The patent applies local quality by differentiating sampling density across different regions of the point cloud data. High-density regions (containing picking targets) are sampled at higher resolution while low-density regions use lower sampling density. This resolves the contradiction by maintaining positioning precision in critical areas while achieving overall processing speed improvement through selective down-sampling of non-critical regions.

The patent implements multi-functionality by using a single imaging device that captures multiple types of information simultaneously (depth data, color data, and spatial coordinates). This single device performs multiple functions that would otherwise require separate sensors, thereby improving picking accuracy through multi-parameter analysis while avoiding the complexity increase that would result from deploying multiple specialized devices.

The patent applies segmentation by dividing the point cloud data processing into distinct stages: initial high-precision positioning phase using full-resolution data to identify picking targets, followed by a refined picking phase where processing is focused only on relevant high-density regions. This segmentation allows the system to maintain positioning precision during target identification while reducing overall processing time by avoiding unnecessary high-resolution processing of entire datasets.

The patent implements dynamics by making the sampling density adaptive rather than static. The system dynamically adjusts sampling density based on detected features and target probabilities in different regions. Areas with high target probability receive dense sampling to ensure detection completeness, while areas with low probability use sparse sampling to minimize data volume. This dynamic adaptation resolves the contradiction by optimizing both detection reliability and data efficiency based on real-time scene analysis.