3D Point Cloud Pose Estimation in Cluttered Object Detection
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Solution Overview
Problem
Current robotic systems face challenges in accurately detecting and estimating the pose of objects in environments with clutter and occlusions, requiring more robust and efficient 3D object recognition and pose determination techniques.
Innovation Solution
The method involves generating an object model based on point cloud data from 3D scanners or CAD models, using scene point cloud data to identify bounding measures, and selecting scene reference points to calculate candidate in-plane rotations and poses, with voting mechanisms to determine the object's pose in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D object detection methods are used in environments with clutter and occlusions, then the system can operate with simpler algorithms, but the detection accuracy and pose estimation reliability deteriorate
Solution Approach 1:
The method segments the object detection problem into multiple candidate pose hypotheses, each generated from different point pair correspondences between model and scene. By evaluating multiple segmented hypotheses rather than a single detection result, the system achieves more reliable pose estimation in cluttered environments where individual detections may be ambiguous.
Solution Approach 2:
The invention transitions from 2D image space to 3D point cloud space for object detection. By working in three-dimensional space with point clouds from both model and scene, the system gains additional spatial dimensions for discrimination, enabling more accurate pose estimation even when objects are occluded or surrounded by clutter.
2Measurement precision
If comprehensive point cloud analysis is performed to improve pose determination accuracy, then the measurement precision improves, but the computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing model point clouds to identify candidate point pairs and their corresponding scene points before actual pose determination. This preliminary organization of data structures enables faster processing during runtime when scene point clouds need to be matched against the model, reducing computational time while maintaining accuracy.
Solution Approach 2:
The method generates multiple candidate pose hypotheses beyond what would be strictly necessary for a single correct answer. By evaluating more candidate poses than the minimum required, the system ensures that the correct pose is identified even when some candidates are spurious, achieving robust accuracy without exhaustive search of all possible poses.
Data Source
AI summary
Methods, apparatus, and computer readable media that are related to 3D object detection and pose determination and that may optionally increase the robustness and/or efficiency of the 3D object recognition and pose determination. Some implementations are generally directed to techniques for generating an object model of an object based on model point cloud data of the object. Some implementations of the present disclosure are additionally and/or alternatively directed to techniques for application of acquired 3D scene point cloud data to a stored object model of an object to detect the object and/or determine the pose of the object.