IP Library › Granted Patent US 12,651,362
Granted Patent B2
US 12,651,362 · App. 18/118,929 · Granted Jun 9, 2026

System and method for validating depth data for a dimensioning operation

Inventors: Sumudu B. Abeysekara (Marassana, LK); Michael Wijayantha Medagama (Nawala, LK)
Assignee: Zebra Technologies Corporation
G06T7/593G06T7/11G06T7/337
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Quick Facts
Patent No.
US 12,651,362
App. No.
18/118,929
Granted
Jun 9, 2026
Kind
B2
Abstract

An example method includes: obtaining, by a depth sensor, depth data representing a distance to a target object and an environment of the target object; selecting a subset of the depth data representing a reference surface in the environment; comparing the subset of the depth data to a flatness condition; when the subset of the depth data does not meet the flatness condition: determining a faulty condition of the depth sensor, and outputting an indicator of the faulty condition of the depth sensor.

Claims (48)

1 . A method comprising:

obtaining, by a depth sensor, depth data representing a distance to a target object and an environment of the target object;

selecting a subset of the depth data representing a reference surface in the environment; and

comparing the subset of the depth data to a flatness condition;

when the subset of the depth data does not meet the flatness condition:

determining a faulty condition of the depth sensor; and

outputting an indicator of the faulty condition of the depth sensor;

wherein selecting the subset of the depth data comprises:

segmenting the depth data into regions; and

applying criteria to one or more parameters of each region to identify one of the regions as the reference surface; and

wherein applying criteria to one or more parameters comprises:

identifying the reference surface as the region having a lowest average height;

identifying the reference surface as the region having a largest area; or

identifying the reference surface as a horizontal region having a height approximately matching a lowest height of the target object.

2 . The method of claim 1 , wherein comparing the subset of the depth data to the flatness condition comprises:

fitting a construct to the subset of the depth data; and

computing a metric representing a quality of fit of the construct to the subset of the depth data; and

when the metric exceeds a predetermined threshold value, determining that the flatness condition is met.

3 . The method of claim 2 , wherein the construct comprises a plane or a line.

4 . The method of claim 2 , further comprising: applying a mask of lines to the subset of the depth data to subsample the subset of the depth data.

5 . The method of claim 2 , wherein the metric comprises one of: a ratio of inliers to outliers and an R-squared value.

6 . The method of claim 1 , further comprising: when the subset of the depth data meets the flatness condition, sending the depth data to a dimensioning application to dimension the target object.

7 . The method of claim 1 , wherein outputting the indicator comprises prompting an operator to clean a lens of the depth sensor.

8 . A system comprising:

a depth sensor configured to obtain depth data representing a distance to a target object and an environment of the target object;

a processor interconnected with the depth sensor, the processor configured to:

select a subset of the depth data representing a reference surface in the environment; and

compare the subset of the depth data to a flatness condition;

when the subset of the depth data does not meet the flatness condition:

determine a faulty condition of the depth sensor; and

output an indicator of the faulty condition of the depth sensor;

wherein to select the subset of the depth data, the processor is configured to:

segment the depth data into regions; and

apply criteria to one or more parameters of each region to identify one of the regions as the reference surface; and

wherein to apply criteria to one or more parameters, the processor is configured to:

identify the reference surface as the region having a lowest average height;

identify the reference surface as the region having a largest area; or

identify the reference surface as a horizontal region having a height approximately matching a lowest height of the target object.

9 . The system of claim 8 , wherein to compare the subset of the depth data to the flatness condition, the processor is configured to:

fit a construct to the subset of the depth data; and

compute a metric representing a quality of fit of the construct to the subset of the depth data; and

when the metric exceeds a predetermined threshold value, determine that the flatness condition is met.

10 . The system of claim 9 , wherein the construct comprises a plane or a line.

11 . The system of claim 9 , wherein the processor is further configured to: apply a mask of lines to the subset of the depth data to subsample the subset.

12 . The system of claim 9 , wherein the metric comprises one of: a ratio of inliers to outliers and an R-squared value.

13 . The system of claim 8 , wherein the processor is further configured to: when the subset of the depth data meets the flatness condition, send the depth data to a dimensioning application to dimension the target object.

14 . The system of claim 8 , wherein to output the indicator, the processor is configured to prompt an operator to clean a lens of the depth sensor.

15 . The system of claim 8 , wherein the depth sensor and the processor are integrated into a dimensioning device.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 5, 2023
From: ABEYSEKARA, SUMUDU B.; MEDAGAMA, MICHAEL WIJAYANTHA
To: ZEBRA TECHNOLOGIES CORPORATION
Reel/Frame 063545/0560 →
Continuity (1)
Related Publication 20240303847A1 · Sep 12, 2024
References Cited (26)
US 9996981B1 · Tran · 2018 [cited by examiner]
US 10593042B1 · Douillard · 2020 [cited by examiner]
US 11836218B2 · Thrimawithana · 2023 [cited by examiner]
US 12307676B2 · Kang · 2025 [cited by examiner]
US 12354215B1 · Langlois · 2025 [cited by examiner]
US 20170023780A1 · Braker · 2017 [cited by examiner]
US 20170124717A1 · Baruch · 2017 [cited by examiner]
US 20180053305A1 · Gu · 2018 [cited by examiner]
US 20180089505A1 · El-Khamy · 2018 [cited by examiner]
US 20190073825A1 · Lee · 2019 [cited by examiner]
US 20200288104A1 · Sheng · 2020 [cited by examiner]
US 20210150227A1 · Hu · 2021 [cited by examiner]
US 20210158558A1 · Thrimawithana · 2021 [cited by examiner]
US 20210167487A1 · Varma · 2021 [cited by examiner]
US 20210168230A1 · Baker · 2021 [cited by examiner]
US 20210168231A1 · Baker · 2021 [cited by examiner]
US 20210241472A1 · Yokoyama · 2021 [cited by examiner]
US 20210343035A1 · Liyanaarachchi · 2021 [cited by examiner]
US 20210374985A1 · Bleicher · 2021 [cited by examiner]
US 20220245882A1 · Spring · 2022 [cited by examiner]
US 20230189982A1 · Rubio · 2023 [cited by examiner]
US 20230237693A1 · Li · 2023 [cited by examiner]
US 20240054670A1 · Medagama · 2024 [cited by examiner]
US 20240193725A1 · Medagama · 2024 [cited by examiner]
US 20250139797A1 · Nuwan Kumara · 2025 [cited by examiner]
WO WO2023023108A1 · 2023 [cited by examiner]