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Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data.

Bryan Rodriguez¹, Prasanna Rangarajan¹, Xinxiang Zhang¹

¹Department of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study uses superquadric fitting to improve object dimensioning with Time-of-Flight (ToF) sensors, achieving less than 1 cm error for cuboids and cylinders despite sensor limitations like noise and low resolution.

Keywords:

3D metrology 3D scanning Time-of-Flight sensors point cloud processing

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Area of Science:

Robotics and Automation
Computer Vision
Metrology

Background:

Time-of-Flight (ToF) sensors offer depth sensing but face challenges like low resolution, noise, and multipath interference.
These limitations distort object shape and size, hindering accurate dimensioning applications.

Purpose of the Study:

To apply a superquadric fitting framework for accurate dimensioning of cuboid and cylindrical objects using ToF sensor data.
To evaluate the framework's performance across various object orientations, ground surfaces, and fitting techniques.

Main Methods:

Utilized point cloud data generated from a ToF sensor.
Applied a superquadric fitting framework to model and dimension cuboid and cylindrical objects.
Investigated bounding and mirroring techniques for model fitting.

Main Results:

Achieved average dimensioning errors below 1 cm for objects up to 30 cm (cuboid) and 20 cm (cylinder) at 1.5 m distance.
Demonstrated absolute dimensioning errors of 4%-9% for cuboids and 2.97%-6.61% for horizontal cylinders using the bounding technique.
Showcased orientation-dependent performance, with vertical cylinders exhibiting higher errors (8.01%-13.13%).

Conclusions:

The superquadric fitting framework effectively overcomes ToF sensor limitations for accurate object dimensioning.
The method provides quantifiable accuracy across different object shapes, orientations, and surface conditions.