# US 12,578,205 B2 · Method and system for automatically generating a map of an indoor space > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,578,205 B2 - Document ID: US12578205B2 - Status: Granted - Kind: B2 - Application number: 17/728,018 - Filed: 2022-04-25 - Published: 2023-10-26 - Granted: 2026-03-17 - Earliest claimed: 2022-04-25 - Assignees: Mapsted Corp. - Applicants: Mapsted Corp - Inventors: Sean Huberman; Jianfeng Hu; Ahmed Mahmood - Web page: https://patentgrep.com/patent/US12578205B2 ## CPC - G01C 21/383: {Indoor data} - G01C 21/3848: {Data obtained from both position sensors and additional sensors} - G01C 21/3867: {Geometry of map features, e.g. shape points, polygons or for simplified maps} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/094: Adversarial learning - G06T 11/00: Two-dimensional [2D] image generation - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06V 10/764: using classification, e.g. of video objects - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 30/422: Technical drawings; Geographical maps - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Examples of generating a map for an indoor space are described. In one example, a raw digital representation of the indoor space is obtained and preprocessed by at least one of marking a region-of-interest (ROI) in the raw digital representation and classifying the indoor space into a space-category to obtain a preprocessed representation. Using the preprocessed representation, a skeletal map is generated including indicators to identify elements of the indoor space. Revision inputs are received to modify an erroneous indicator from amongst the multiple indicators. The erroneous indicator is revised to a modified indicator and the modified indicator and unmodified indicators are used to create a modified skeletal map which is usable for localizing a mobile device in the indoor space. ## Claims ### Claim 1 A method of automatically generating a map of an indoor space, the method comprising: obtaining a raw digital representation of the indoor space; preprocessing the raw digital representation to obtain a preprocessed representation by: marking a region-of-interest (ROI) in the raw digital representation, the marking includes: identifying extraneous matter in the raw digital representation, the extraneous matter comprising matter not relevant to a floor plan of the indoor space; discarding the extraneous matter to select a portion of the floor plan in the raw digital representation; and identifying a space-category of the indoor space based on a layout of the indoor space and identification of entities of the indoor space from the raw digital representation to classify the indoor space into the space category; generating, from the preprocessed representation, a skeletal map of the indoor space comprising a plurality of indicators each to identify a structural element of the indoor space; overlaying the skeletal map having georeferenced plurality of indicators with a fingerprint map for generating a map for the indoor space, the fingerprint map comprising a correlation of sensor and signal information for a sequence of positions that constitute a navigation path traversed by a mobile device relative to the indoor space; receiving revision inputs to correct an incorrectly identified indicator from amongst the plurality of indicators, wherein the indicator is revised to a modified indicator; and creating a modified skeletal map of the indoor space comprising the modified indicator and unmodified indicators, wherein the modified skeletal map is usable with a localization application for localizing the mobile device in the indoor space. ### Claim 2 (depends on 1) The method of claim 1, executed by a trained machine-learning model. ### Claim 3 (depends on 2) The method of claim 2, wherein the trained machine-learning model is a trained deep-learning model. ### Claim 4 (depends on 2) The method of claim 2, wherein the modified indicator is fed to the trained machine-learning model as training data. ### Claim 5 (depends on 1) The method of claim 1, wherein the raw digital representation is one of a georeferenced vectored representation, a georeferenced rasterized representation, a non-georeferenced vectored representation, and non-a georeferenced rasterized representation. ### Claim 6 (depends on 1) The method of claim 1, wherein the preprocessing comprises converting the raw digital representation into a predefined standardized format. ### Claim 7 (depends on 1) The method of claim 1, wherein the preprocessing comprises performing at least one of resolution enhancement, noise reduction, and image augmentation on the raw digital representation. ### Claim 8 (depends on 1) The method of claim 1, wherein the generating comprises applying a feature detection technique, the feature detection technique comprising at least one of an edge detection technique and a corner detection technique. ### Claim 9 (depends on 1) The method of claim 1, wherein the skeletal map is in a rasterized representation. ### Claim 10 (depends on 9) The method of claim 9, wherein the creating comprises converting the rasterized representation into a vectored representation. ### Claim 11 A server computing device for automatically generating a map of an indoor space, the server computing device comprising: a processor; and a memory storing a set of instructions, the instructions executable in the processor to: obtain a raw digital representation of the indoor space; preprocess the raw digital representation to obtain a preprocessed representation by: marking a region-of-interest (ROI) in the raw digital representation, the marking includes: identifying extraneous matter in the raw digital representation, the extraneous matter comprising matter not relevant to a floor plan of the indoor space; discarding the extraneous matter to select a portion of the floor plan in the raw digital representation; and identifying into a space-category of the indoor space based on a layout of the indoor space and identification of entities of the indoor space from the raw digital representation to classify the indoor space into the space category; generate, from the preprocessed representation, a skeletal map of the indoor space comprising a plurality of indicators each to identify a structural element of the indoor space; overlay the skeletal map having georeferenced plurality of indicators with a fingerprint map for generating a map for the indoor space, the fingerprint map comprising a correlation of sensor and signal information for a sequence of positions that constitute a navigation path traversed by a mobile device relative to the indoor space; receive revision inputs to correct an incorrectly identified indicator from amongst the plurality of indicators, wherein the indicator is revised to a modified indicator; and create a modified skeletal map of the indoor space comprising the modified indicator and unmodified indicators, wherein the modified skeletal map is usable with a localization application for localizing a mobile device in the indoor space. ### Claim 12 (depends on 11) The server computing device of claim 11 implementing a trained machine-learning model. ### Claim 13 (depends on 12) The server computing device of claim 12 further comprising instructions to feed the modified indicator to the trained machine-learning model as training data. ### Claim 14 (depends on 12) The server computing device of claim 12, wherein the trained machine-learning model is a trained deep-learning model. ### Claim 15 (depends on 11) The server computing device of claim 11, wherein the raw digital representation is one of a georeferenced vectored representation, a georeferenced rasterized representation, a non-georeferenced vectored representation, and non-a georeferenced rasterized representation. ### Claim 16 (depends on 11) The server computing device of claim 11 further comprising instructions to convert the raw digital representation into a pre-defined standardized format. ### Claim 17 (depends on 11) The server computing device of claim 11 further comprising instructions to perform at least one of resolution enhancement, noise reduction, and image augmentation on the raw digital representation. ### Claim 18 (depends on 11) The server computing device of claim 11 further comprising instructions to apply a feature detection technique, the feature detection technique comprising at least one of an edge detection technique and a corner detection technique. ### Claim 19 (depends on 11) The server computing device of claim 11, wherein the skeletal map is in a rasterized representation. ### Claim 20 (depends on 19) The server computing device of claim 19, further comprising instructions to convert the rasterized representation into a vectored representation.