Rigid Object Detection Using Temporal Point Cloud Segmentation
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Solution Overview
Problem
Existing systems struggle to accurately detect and identify rigid shaped objects in environments using point cloud representations due to overlapping structures and inaccurate association of data points, leading to incorrect object recognition.
Innovation Solution
A computing system uses depth sensors to determine initial and subsequent point cloud representations of an environment, detecting changes in position and configuration of data points to identify rigid shapes, and stores information about detected objects by comparing them to a database and obtaining additional information from nearby devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If point cloud representations are used to detect objects in environments, then object detection capability is provided, but overlapping structures cause inaccurate association of data points leading to incorrect object recognition
Solution Approach 1:
The system segments the environment into multiple point cloud representations captured at different times, allowing individual data points to be tracked and associated across time segments. This temporal segmentation resolves the overlapping structure problem by providing sequential views that separate previously overlapping objects.
Solution Approach 2:
The system performs preliminary detection of rigid shapes in the first point cloud representation before final object recognition. By pre-identifying potential rigid shapes and tracking them through subsequent point cloud representations, the system establishes preliminary associations that are refined over time, improving final recognition accuracy.
2Measurement precision
If multiple point cloud representations are used to improve object recognition accuracy, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system uses a limited number of point cloud representations (first and second time points) rather than continuous processing. This partial action approach provides sufficient accuracy improvement while avoiding the excessive processing time that would result from analyzing every available time point.
Solution Approach 2:
The system skips intermediate processing steps by directly comparing rigid shape positions between the first and second point cloud representations. Rather than performing exhaustive analysis of all data points at each time point, the system efficiently transitions from initial detection to final recognition by focusing only on identified rigid shapes.
Data Source
AI summary
Examples relating to the detection of rigid shaped objects are described herein. An example method may involve a computing system determining a first point cloud representation of an environment at a first time using a depth sensor positioned within the environment. The computing system may also determine a second point cloud representation of the environment at a second time using the depth sensor. This way, the computing system may detect a change in position of a rigid shape between a first position in the first point cloud representation and a second position in the second point cloud representation. Based on the detected change in position of the rigid shape, the computing system may determine that the rigid shape is representative of an object in the environment and store information corresponding to the object.