Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data
One of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that the depth information suffers from issues such as low resolution, self-occlusions, noise, and multipath interference, which distort the shape and size of objects. In this work, we successfully apply a superq...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8673 |
| _version_ | 1797631369354936320 |
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| author | Bryan Rodriguez Prasanna Rangarajan Xinxiang Zhang Dinesh Rajan |
| author_facet | Bryan Rodriguez Prasanna Rangarajan Xinxiang Zhang Dinesh Rajan |
| author_sort | Bryan Rodriguez |
| collection | DOAJ |
| description | One of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that the depth information suffers from issues such as low resolution, self-occlusions, noise, and multipath interference, which distort the shape and size of objects. In this work, we successfully apply a superquadric fitting framework for dimensioning cuboid and cylindrical objects from point cloud data generated using a ToF sensor. Our work demonstrates that an average error of less than 1 cm is possible for a box with the largest dimension of about 30 cm and a cylinder with the largest dimension of about 20 cm that are each placed 1.5 m from a ToF sensor. We also quantify the performance of dimensioning objects using various object orientations, ground plane surfaces, and model fitting methods. For cuboid objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 4% and 9% using the bounding technique and between 8% and 15% using the mirroring technique across all tested surfaces. For cylindrical objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 2.97% and 6.61% when the object is in a horizontal orientation and between 8.01% and 13.13% when the object is in a vertical orientation using the bounding technique across all tested surfaces. |
| first_indexed | 2024-03-11T11:21:54Z |
| format | Article |
| id | doaj.art-f3a430d2359d406ea018b8905e07282c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:54Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f3a430d2359d406ea018b8905e07282c2023-11-10T15:11:39ZengMDPI AGSensors1424-82202023-10-012321867310.3390/s23218673Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight DataBryan Rodriguez0Prasanna Rangarajan1Xinxiang Zhang2Dinesh Rajan3Department of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USADepartment of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USADepartment of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USADepartment of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USAOne of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that the depth information suffers from issues such as low resolution, self-occlusions, noise, and multipath interference, which distort the shape and size of objects. In this work, we successfully apply a superquadric fitting framework for dimensioning cuboid and cylindrical objects from point cloud data generated using a ToF sensor. Our work demonstrates that an average error of less than 1 cm is possible for a box with the largest dimension of about 30 cm and a cylinder with the largest dimension of about 20 cm that are each placed 1.5 m from a ToF sensor. We also quantify the performance of dimensioning objects using various object orientations, ground plane surfaces, and model fitting methods. For cuboid objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 4% and 9% using the bounding technique and between 8% and 15% using the mirroring technique across all tested surfaces. For cylindrical objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 2.97% and 6.61% when the object is in a horizontal orientation and between 8.01% and 13.13% when the object is in a vertical orientation using the bounding technique across all tested surfaces.https://www.mdpi.com/1424-8220/23/21/86733D scanning3D metrologypoint cloud processingTime-of-Flight sensors |
| spellingShingle | Bryan Rodriguez Prasanna Rangarajan Xinxiang Zhang Dinesh Rajan Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data Sensors 3D scanning 3D metrology point cloud processing Time-of-Flight sensors |
| title | Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data |
| title_full | Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data |
| title_fullStr | Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data |
| title_full_unstemmed | Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data |
| title_short | Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data |
| title_sort | dimensioning cuboid and cylindrical objects using only noisy and partially observed time of flight data |
| topic | 3D scanning 3D metrology point cloud processing Time-of-Flight sensors |
| url | https://www.mdpi.com/1424-8220/23/21/8673 |
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