Mobile Depth Sensing for Multipath-Resistant Object Dimensioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Depth sensors in mobile devices suffer from multipath reflections that distort point clouds, leading to inaccurate object dimensions due to the integration of multiple reflections, which complicates the determination of precise three-dimensional positions.
Innovation Solution
A mobile computing device equipped with a motion sensor and a depth sensor, such as a ToF sensor, captures multiple point clouds while monitoring orientation changes, and assesses attribute consistency to identify and mitigate multipath artifacts by selecting a point cloud with consistent attributes, thereby improving dimension accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a depth sensor captures point clouds to determine object dimensions, then dimensioning capability is enabled, but multipath reflections cause artifacts that reduce measurement precision
Solution Approach 1:
The system performs preliminary actions by capturing multiple point clouds from different device orientations before final dimension determination. By proactively collecting redundant data from varied perspectives, the system prepares to identify and eliminate multipath artifacts through comparison, ensuring measurement precision without being compromised by reflection artifacts in any single capture.
Solution Approach 2:
The system implements feedback by comparing attributes across multiple point clouds captured from different orientations. When attribute variations exceed a threshold, the system identifies potential multipath artifacts and uses the comparison feedback to select the most reliable point cloud for dimensioning, thereby overcoming the harmful effects of reflections through iterative validation.
2Measurement precision
If multiple point clouds are captured from different orientations to mitigate artifacts, then measurement precision improves, but the time required for dimensioning increases
Solution Approach 1:
The system applies partial action by capturing a predetermined number of point clouds from different orientations rather than exhaustively capturing from all possible angles. This selective approach provides sufficient data to identify and mitigate multipath artifacts through attribute comparison while avoiding excessive time consumption that would result from comprehensive multi-angle capture.
Solution Approach 2:
The system changes parameters by monitoring device orientation and using orientation variation as a control parameter for capturing diverse point clouds. By leveraging natural orientation changes and comparing attributes across these variations, the system efficiently identifies artifacts without requiring time-consuming manual repositioning or exhaustive sampling, thus balancing precision with time efficiency.
3Measurement precision
If attribute comparison threshold is set low to ensure precision, then measurement accuracy improves, but the complexity of the processing increases
Solution Approach 1:
The system introduces an intermediary mechanism by using device orientation data as a mediator between multiple point cloud captures. Instead of directly comparing all attributes of all point clouds (which would be computationally intensive), the system uses orientation information to guide selective comparison, reducing processing complexity while maintaining precision through threshold-based attribute consistency verification.
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
The method enhances the accuracy of object dimensions by reducing the impact of multipath artifacts through iterative point cloud capture and orientation adjustment, ensuring precise measurements.
Implementation Method 1
Depth sensors such as time-of-flight (ToF) sensors can be deployed in mobile devices
Implementation Method 2
monitoring, via a motion sensor, an orientation of the depth sensor
Data Source
AI summary
A method in a computing device includes: capturing, via a depth sensor, a first point cloud depicting an object; determining, from the first point cloud, a first attribute of a plane corresponding to a surface of the object; monitoring, via a motion sensor, an orientation of the depth sensor; in response to detecting a change in the orientation that meets a threshold, capturing a second point cloud depicting the object; determining, from the second point cloud, a second attribute of the plane corresponding to the surface of the object; determining whether the first attribute and the second attribute match; and when the first attribute and the second attribute match, dimensioning the object based on at least one of the first point cloud and the second point cloud.