Continuous Surface Perception for Tracking Object Motion
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Solution Overview
Problem
Conventional machine vision systems lack inherent support for continuous surface representation of objects, relying on inaccurate or unrepresentative scene data for analysis, which affects robotic perception and control.
Innovation Solution
Employing a system that generates trajectories based on a continuous stream of sensor events to represent three-dimensional surfaces, using scanning signal generators and sensors to detect reflected energy, and a modeling engine to determine and update surface changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine vision systems use 2D pixel arrays or 3D point clouds for object perception, then the system can capture basic scene data, but the representation lacks continuous surface information and temporal evolution, leading to inaccurate perception
Solution Approach 1:
The system transitions from static 2D/3D representations to dynamic surface representations that continuously update over time. Sensors capture temporal sequences of surface data, and the system models surface evolution dynamically, allowing accurate tracking of moving objects and deformable surfaces while maintaining continuous representation.
Solution Approach 2:
The invention adds the temporal dimension to traditional 2D image or 3D point cloud representations by introducing time-varying surface parameters. This creates a 4D representation (x, y, z, t) that captures not only spatial geometry but also how surfaces evolve over time, enabling continuous surface tracking.
2Device complexity
If machine vision systems process static scene data captured from sensors, then the processing pipeline is simpler, but fundamental scene information is filtered out before analysis, reducing perception accuracy
Solution Approach 1:
The system performs preliminary processing by establishing continuous surface models from sensor data before higher-level analysis. By pre-computing surface trajectories and temporal evolution patterns, the system prepares rich structural information in advance, making subsequent object recognition and tracking more reliable without requiring overly complex real-time processing.
Solution Approach 2:
The system uses feedback from continuous surface tracking to refine perception results. By monitoring how surfaces evolve over time and comparing against expected patterns, the system can correct errors and improve reliability of object detection and classification while maintaining a manageable processing pipeline.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and continuous perception of object surfaces and motion, improving robotic planning and control by providing a native representation of evolving surfaces and objects.
Implementation Method 1
sensors arranged to provide a stream of sensor events based on where the energy from the scanning signal generator reflects from a surface
Data Source
AI summary
Embodiments are directed to perceiving surfaces and objects. Trajectories may be generated based on a continuous stream of sensor events such that each trajectory may be a parametric representation of a curve segment. The trajectories may be employed to determine the surfaces. The trajectories may be provided to a modeling engine to execute one or more actions based on the trajectories and the surfaces. In response to changes to the surfaces, the trajectories may be updated based on the continuous stream of sensor events and one or more additional actions may be executed based on the updated trajectories and the changed surfaces. Changes to the surfaces may include a position change, an orientation change, a motion change, a deformation of the one or more surfaces, or the like. Shapes that correspond to the surfaces may be determined based on characteristics of the surfaces or the trajectories.