Abstract: One variation of a method for managing virtual shopping lists includes: dispatching robotic systems, deployed in store, to autonomously scan inventory structures within this store; deriving current stock conditions of this store based on scan data recorded by these robotic systems; initializing a virtual shopping list for a user; in response to receipt of selection of a first product, from a population of products, isolating a subset of stores, in the set of stores in the geographic region, associated with current stock conditions indicating presence of the first product and products previously added to the virtual shopping list; in response to the subset of stores including at least one store, adding a first identifier of the first product to the virtual shopping list; and specifying a particular store, in the subset of stores, for fulfillment of a set of products on the virtual shopping list.

Abstract: One variation of a method for maintaining perpetual inventory within a store includes: accessing a radar scan of an inventory structure within a store; accessing an optical image of the inventory structure; identifying a product type associated with the slot in a region of the optical image; retrieving a volumetric definition of the product type; locating a slot volume defining the slot in the radar scan; extracting a volumetric representation of a set of product units intersecting the slot volume in the radar scan; segmenting the volumetric representation by the volumetric definition to calculate a quantity of the set of product units occupying the slot; and updating a stock record of the store to reflect the quantity of the set of product units occupying the slot.

Abstract: One variation of a method includes: accessing an image of an inventory structure captured by a robotic system navigating within a facility; detecting an object occupying a slot in the inventory structure depicted in the image; extracting a set of visual features from the image; representing the set of visual features in a vector; projecting the vector into a multi-dimensional space populated template vectors representing product units of verified product types within the facility; calculating a similarity score between the set of visual features and template visual features represented in a cluster of template vectors in the multi-dimensional space, based on proximity between the vector and the cluster of template vectors; and, in response to the similarity score exceeding a threshold score, identifying the object as a product unit of a first product type affiliated with a first product identifier associated with the cluster of template vectors.

Abstract: One variation of a method includes: receiving a first image captured by a mobile device in a back-of-store inventory region of a store; identifying a product type represented in the first image; accessing a second image of an inventory structure in a customer region of the store, the second image captured by a mobile robotic system traversing aisles of the store during a scan cycle; based on features extracted from the second image and the set of point-of-sale data, predicting a first quantity of product units of the product type loaded in the slot; and, in response to the first quantity of product units falling below a threshold quantity, generating a prompt to deliver a second quantity of product units from the back-of-store inventory structure to the slot in the inventory structure in the customer region of the store; and transmitting the prompt to the mobile device.

Abstract: One variation of a method for segmenting scenes of product units arranged in inventory structures within a store includes: accessing an image based on data captured by a mobile robotic system; detecting a shelving segment in the image; reading a segment identifier from a segment tag, detected in the image, arranged on the shelving segment; accessing a product template representing a product type in the set of product types assigned to the shelving segment based on the segment identifier; detecting a set of product features, in the first region of the image. In response to detecting the set of product features analogous to features of the product template: confirming presence of the unit of the first product type on the shelf in the shelving segment and appending the first product type to a list of product types presently stocked in the shelving segment.

Abstract: One variation of a method for managing virtual shopping lists includes: dispatching robotic systems, deployed in store, to autonomously scan inventory structures within this store; deriving current stock conditions of this store based on scan data recorded by these robotic systems; initializing a virtual shopping list for a user; in response to receipt of selection of a first product, from a population of products, isolating a subset of stores, in the set of stores in the geographic region, associated with current stock conditions indicating presence of the first product and products previously added to the virtual shopping list; in response to the subset of stores including at least one store, adding a first identifier of the first product to the virtual shopping list; and specifying a particular store, in the subset of stores, for fulfillment of a set of products on the virtual shopping list.

Abstract: One variation of a method includes: accessing an image of an inventory structure captured by a robotic system navigating within a facility; detecting an object occupying a slot in the inventory structure depicted in the image; extracting a set of visual features from the image; representing the set of visual features in a vector; projecting the vector into a multi-dimensional space populated template vectors representing product units of verified product types within the facility; calculating a similarity score between the set of visual features and template visual features represented in a cluster of template vectors in the multi-dimensional space, based on proximity between the vector and the cluster of template vectors; and, in response to the similarity score exceeding a threshold score, identifying the object as a product unit of a first product type affiliated with a first product identifier associated with the cluster of template vectors.

Abstract: A method includes: locating a first slot boundary around a first product unit, in an image of an inventory structure in a store, identifies as a first product type based on features detected in the image; extracting a first product identifier, from a first slot tag, detected in the image; based on correspondence between the first product identifier and 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 slot tag from the image; extracting a second product identifier from a second slot tag detected in the image; predicting a second slot boundary, assigned to a second product type corresponding to the second product identifier, in the image based on a second position of the second tag in the image and the first relative tag-boundary position.

Abstract: One variation of a method includes: accessing a sequence of images of an inventory structure captured by a mobile robotic system during a scan cycle within a facility; detecting a set of features, representing a product descriptor, proximal a slot in an image in the sequence of images; retrieving a set of template features of a product type associated with the product descriptor from a database of template features; detecting absence of product units of the product type in the slot based on absence of features analogous to the set of template features in the image; constructing a three-dimensional image of the inventory structure based on the sequence of images; annotating the three-dimensional image with a marker representing absence of product units of the product type in the slot; and serving the three-dimensional image of the inventory structure to a portal accessed by an associate affiliated with the facility.

Abstract: One variation of a method includes: accessing an image of an inventory structure captured by a robotic system navigating within a facility; detecting an object occupying a slot in the inventory structure depicted in the image; extracting a set of visual features from the image; representing the set of visual features in a vector; projecting the vector into a multi-dimensional space populated template vectors representing product units of verified product types within the facility; calculating a similarity score between the set of visual features and template visual features represented in a cluster of template vectors in the multi-dimensional space, based on proximity between the vector and the cluster of template vectors; and, in response to the similarity score exceeding a threshold score, identifying the object as a product unit of a first product type affiliated with a first product identifier associated with the cluster of template vectors.

Abstract: One variation of a method for maintaining perpetual inventory within a store includes: accessing a radar scan of an inventory structure within a store; accessing an optical image of the inventory structure; identifying a product type associated with the slot in a region of the optical image; retrieving a volumetric definition of the product type; locating a slot volume defining the slot in the radar scan; extracting a volumetric representation of a set of product units intersecting the slot volume in the radar scan; segmenting the volumetric representation by the volumetric definition to calculate a quantity of the set of product units occupying the slot; and updating a stock record of the store to reflect the quantity of the set of product units occupying the slot.

Abstract: One variation of a method for deploying a mobile robotic system to scan inventory structures within a store includes: dispatching the mobile robotic system to navigate along inventory structures within the store during a setup cycle; at the mobile robotic system, while navigating along the inventory structures during the setup cycle, capturing a set of wireless connectivity metrics representing connectivity to a first wireless network; assembling the set of wireless connectivity metrics into a wireless connectivity map of the store; estimating a processing duration from start of the scan cycle to transformation of images of the inventory structures, captured by the mobile robotic system, into a stock condition of the store; and dispatching the mobile robotic system to autonomously capture images of the inventory structures within the store during a scan cycle preceding a scheduled restocking period in the store based on the processing duration.

Abstract: One variation of a method includes: accessing an image of an inventory structure captured by a robotic system while navigating through a store; detecting a slot in the inventory structure in the image; based on features extracted from the image, identifying a set of product units of a first product type occupying the slot; accessing a target product type assigned to the slot by a store representation; based on features extracted from the image, identifying an electronic shelf label depicted in the image, corresponding to the slot, and advertising the target product type; and, in response to the first product type differing from the target product type assigned to the slot, accessing a set of product data corresponding to the first product type from a product database, and, transmitting the set of product data to the electronic shelf label for rendering within an electronic display of the electronic shelf label.

Abstract: One variation of a method for segmenting scenes of product units arranged in inventory structures within a store includes: accessing an image based on data captured by a mobile robotic system; detecting a shelving segment in the image; reading a segment identifier from a segment tag, detected in the image, arranged on the shelving segment; accessing a product template representing a product type in the set of product types assigned to the shelving segment based on the segment identifier; detecting a set of product features, in the first region of the image. In response to detecting the set of product features analogous to features of the product template: confirming presence of the unit of the first product type on the shelf in the shelving segment and appending the first product type to a list of product types presently stocked in the shelving segment.

Abstract: One variation of a method for tracking placement of products in a store includes: accessing an image recorded by a mobile robotic system within a store; detecting a shelf in a region of the image; based on an address of the shelf, retrieving a list of products assigned to the shelf by a planogram of the store; retrieving a set of template images—from a database of template images—defining visual features of products specified in the list of products; extracting a set of features from the region of the image; determining that a unit of the product is mis-stocked on the shelf in response to deviation between the set of features and features in a template image, in the set of template images, representing the product; and in response to determining that the unit of the product is mis-stocked on the shelf, generating a restocking prompt for the product.

Abstract: One variation of a method for deploying a mobile robotic system to scan inventory structures within a store includes: dispatching the mobile robotic system to navigate along inventory structures within the store during a setup cycle; at the mobile robotic system, while navigating along the inventory structures during the setup cycle, capturing a set of wireless connectivity metrics representing connectivity to a first wireless network; assembling the set of wireless connectivity metrics into a wireless connectivity map of the store; estimating a processing duration from start of the scan cycle to transformation of images of the inventory structures, captured by the mobile robotic system, into a stock condition of the store; and dispatching the mobile robotic system to autonomously capture images of the inventory structures within the store during a scan cycle preceding a scheduled restocking period in the store based on the processing duration.

Abstract: One variation of a method for tracking placement of products in a store includes: accessing an image recorded by a mobile robotic system within a store; detecting a shelf in a region of the image; based on an address of the shelf, retrieving a list of products assigned to the shelf by a planogram of the store; retrieving a set of template images—from a database of template images—defining visual features of products specified in the list of products; extracting a set of features from the region of the image; determining that a unit of the product is mis-stocked on the shelf in response to deviation between the set of features and features in a template image, in the set of template images, representing the product; and in response to determining that the unit of the product is mis-stocked on the shelf, generating a restocking prompt for the product.

Abstract: One variation of a method for stock keeping in a store includes: accessing an image captured by a fixed camera within the store; retrieving a field of view of the fixed camera; estimating a segment of an inventory structure in the store depicted in the image based on a projection of the field of view onto a planogram of the store; identifying a set of slots within the inventory structure segment; retrieving a product model representing a set of visual characteristics of a product type assigned to a slot, in the set of slots, by the planogram; extracting a constellation of features from the image; if the constellation of features approximates the set of visual characteristics in the product model, detecting presence of a product unit of the product type occupying the inventory structure segment; and representing presence of the product unit, occupying the inventory structure segment, in a realogram.

Abstract: One variation of a method for tracking and maintaining inventory in a store includes: accessing a image of an inventory structure in the store; identifying a top shelf, in the inventory structure, depicted in the image; identifying a set of product units occupying the top shelf based on features detected in the image; identifying a second shelf, in the set of shelves in the inventory structure, depicted in the image, the second shelf arranged below the top shelf in the inventory structure; based on features detected in the image, detecting an understock condition at a slot-assigned to a product type-on the second shelf; and, in response to the set of product units comprising a product unit of the product type, generating a prompt to transfer the product unit of the product type from the top shelf into the slot on the second shelf at the inventory structure.

Abstract: One variation of a method for stock keeping in a store includes: accessing an image captured by a fixed camera within the store; retrieving a field of view of the fixed camera; estimating a segment of an inventory structure in the store depicted in the image based on a projection of the field of view onto a planogram of the store; identifying a set of slots within the inventory structure segment; retrieving a product model representing a set of visual characteristics of a product type assigned to a slot, in the set of slots, by the planogram; extracting a constellation of features from the image; if the constellation of features approximates the set of visual characteristics in the product model, detecting presence of a product unit of the product type occupying the inventory structure segment; and representing presence of the product unit, occupying the inventory structure segment, in a realogram.