3D Point Cloud Object Recognition for Accurate Pose Estimation
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Solution Overview
Problem
Existing object recognition systems struggle to accurately specify the position and pose of multiple types of target objects, especially when not sufficiently trained, and may fail to identify regions of target objects in complex environments.
Innovation Solution
An object recognition device that utilizes a specification unit to identify an object region based on a three-dimensional point cloud and feature information, and an estimation unit to estimate the position and pose of the target object using a reference shape, enabling accurate recognition even with multiple types of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning-based object recognition is used to identify multiple types of objects, then object classification capability is improved, but recognition accuracy deteriorates when the system is not sufficiently trained
Solution Approach 1:
The patent segments the object recognition process into two distinct stages: (1) a rough recognition stage using deep learning to identify candidate object types and regions, and (2) a precise recognition stage using template matching with extracted features to accurately determine object positions and types. This segmentation allows the system to leverage the versatility of deep learning while avoiding its accuracy limitations when insufficiently trained, by using it only for initial candidate selection rather than final precise recognition.
Solution Approach 2:
The patent performs preliminary extraction of object region features (such as edge information, contour data, or texture characteristics) from the distance image before the final recognition step. These pre-extracted features are then used in template matching to achieve accurate object identification. This preliminary action prepares the data in advance, enabling the second-stage precise recognition to work more effectively and achieve higher accuracy even when the deep learning model is not fully trained.
2Measurement precision
If template matching is used for precise object recognition, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the recognition workflow into two phases with different accuracy-time tradeoffs: the first phase uses faster deep learning-based rough recognition to identify candidate objects, and the second phase applies more computationally intensive template matching only to these limited candidates. This segmentation ensures that the time-consuming precise recognition operations are performed on a small subset of candidates rather than all detected objects, thereby reducing overall processing time while maintaining high accuracy for final recognition results.
Solution Approach 2:
The patent applies template matching (a computationally expensive operation) only partially - specifically, only to candidate objects identified in the rough recognition stage, rather than applying it to all objects in the scene. This partial application of the precise recognition method reduces the total computational burden and processing time while still achieving high accuracy for the objects that matter most - the actual target objects that passed the initial filtering stage.
3Reliability
If feature extraction is performed on all detected objects, then recognition completeness is improved, but computational complexity increases
Solution Approach 1:
The patent segments the feature extraction process by applying it only to candidate objects identified in the rough recognition stage, rather than performing feature extraction on all detected objects in the scene. This segmentation of the processing pipeline ensures that computational resources are focused on objects that are likely to be relevant targets, maintaining recognition completeness for actual objects while reducing the overall computational complexity by excluding background and irrelevant elements from the expensive feature extraction and template matching processes.
Data Source
AI summary
An object recognition device being able to accurately specify a position and pose of a target object is provided. The object recognition device includes: a specification unit that specifies an object region including a three-dimensional point cloud indicating a position of a surface of a target object, based on a three-dimensional point cloud generated by converting a distance image including distance information to the target object, and specifies a reference shape similar to the target object, based on a three-dimensional point cloud included in the object region and feature information about a reference shape; and an estimation unit that estimates a reference position and a pose of the target object, based on a three-dimensional point cloud included in the object region and the specified reference shape.