Method for classifying product organization in an inventory structure

A method comprising:

• by a mobile robotic system:
• autonomously navigating throughout a store during a scan cycle;
• and
• via an optical sensor integrated in the mobile robotic system, capturing images of inventory structures in the store during the scan cycle;
• and
• by a computer system:
• accessing a first image depicting an inventory structure in a store at a first time, the first image captured by the mobile robotic system during the scan cycle;
• detecting a first product unit in the first image;
• extracting a first set of features, depicting the first product unit, from the first image;
• identifying the first product unit as a first product type based on the first set of features;
• locating a first slot boundary, containing the first product unit, in the first image;
• detecting a first slot tag in the first image;
• extracting a first product identifier, from the first slot tag, depicted in the first image;
• in response to the first product identifier corresponding to the first product type:
• associating the first slot boundary with the first slot tag;
• and
• extracting a first relative tag-boundary position of the first slot boundary and the first slot tag from the first image;
• detecting a second product unit in the first image;
• extracting a second set of features, depicting the second product unit, from the first image;
• identifying the second product unit as a second product type based on the second set of features;
• locating a second slot boundary, containing the second product unit, in the first image;
• detecting a second slot tag in the first image;
• extracting a second product identifier, from the second slot tag, depicted in the first image;
• in response to the second product identifier corresponding to the second product type, associating the second slot boundary with the second slot tag;
• detecting a third slot tag in the first image;
• extracting a third product identifier, from the third slot tag, depicted in the first image;
• characterizing a first distance between the first slot tag and the second slot tag;
• characterizing a second distance between the second slot tag and the third slot tag;
• and
• in response to absence of the third product unit corresponding to the third product identifier within the third slot boundary in the first image and in response to the second distance exceeding the first distance:
• predicting a third slot boundary, assigned to a third product type corresponding to the third product identifier, in the first image based on:
• a third position of the third tag in the first image;
• and
• the first relative tag-boundary position.

A method comprising:

• by a mobile robotic system:
• autonomously navigating throughout a store during a scan cycle;
• and
• via an optical sensor integrated in the mobile robotic system, capturing images of inventory structures in the store during the scan cycle;
• and
• by a computer system:
• accessing a first image depicting an inventory structure in a store at a first time, the first image captured by the mobile robotic system during the scan cycle;
• detecting a first product unit in the first image;
• extracting a first set of features, depicting the first product unit, from the first image;
• identifying the first product unit as a first product type based on the first set of features;
• locating a first slot boundary, containing the first product unit, in the first image;
• detecting a first slot tag in the first image;
• extracting a first product identifier, from the first slot tag, depicted in the first image;
• in response to the first product identifier corresponding to the first product type:
• associating the first slot boundary with the first slot tag;
• extracting a first relative tag-boundary position of the first slot boundary and the first slot tag from the first image, the first relative tag-boundary position defining a hang tag location centered above the first slot boundary based on positions of the first slot boundary and the first slot tag in the first image;
• and
• identifying the first slot boundary as in-stock at the first time;
• detecting a second product unit in the first image;
• extracting a second set of features, depicting the second product unit, from the first image;
• identifying the second product unit as a second product type based on the second set of features;
• locating a second slot boundary, containing the second product unit, in the first image;
• locating a second location of a second slot tag, corresponding to the second slot boundary adjacent and centered above the second slot boundary in the first image;
• and
• in response to detecting absence of the second slot tag in the second location in the first image, flagging the second slot boundary as missing the second slot tag.