# US 11,443,131 B2 · Systems and methods for creating a parking map > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,131 B2 - Document ID: US11443131B2 - Status: Granted - Kind: B2 - Application number: 16/879,239 - Filed: 2020-05-20 - Published: 2021-11-25 - Granted: 2022-09-13 - Earliest claimed: 2020-05-20 - Assignees: Toyota Motor Engineering & Manufacturing North America, Inc. - Applicants: Toyota Motor Engineering & Manufacturing North America, Inc. - Inventors: Takamasa Higuchi; Kentaro Oguchi - Web page: https://patentgrep.com/patent/US11443131B2 ## CPC - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06F 18/23: Clustering techniques - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method of creating a parking facility map includes obtaining one or more images of a parking facility, generating an estimate map of parking spaces in the parking facility based at least on the one or more images (the estimate map of parking spaces includes a plurality of estimate parking space shapes), identifying a parking block that includes a cluster of estimate parking spaces among the plurality of estimate parking space shapes, determining a common geometric pattern among the cluster of estimate parking spaces, correcting geometrical features of one or more estimate parking spaces in the cluster of estimate parking spaces based at least in part on the common geometric pattern, identifying one or more gaps among the cluster of estimate parking space shapes, and determining a number of inserted parking spaces to fill the one or more gaps based at least in part on the common geometric pattern. ## Claims ### Claim 1 A parking facility mapping system, comprising one or more processors; and a memory communicably connected to the one or more processors and storing: an image module including one or more instructions that, when executed by the one or more processors, cause the one or more processors to obtain one or more images of a parking facility; an estimate module including one or more instructions that, when executed by the one or more processors, cause the one or more processors to generate an estimate map of parking spaces in the parking facility based at least on the one or more images, the estimate map of parking spaces including a plurality of estimate parking space shapes; and a correction module including one or more instructions that, when executed by the one or more processors, cause the one or more processors to identify a parking block that includes a cluster of estimate parking spaces among the plurality of estimate parking space shapes, determine a common geometric pattern among the cluster of estimate parking spaces, correct geometrical features of one or more estimate parking spaces in the cluster of estimate parking spaces based at least in part on the common geometric pattern, identify one or more gaps among the cluster of estimate parking spaces, and determine a number of inserted parking spaces to fill the one or more gaps based at least in part on the common geometric pattern to create a corrected map of parking spaces of the parking facility. ### Claim 2 (depends on 1) The parking facility mapping system of claim 1, wherein, the correction module further includes instructions to identify the parking block by executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in orientation angles of estimate parking spaces within the same cluster being less than a predefined threshold. ### Claim 3 (depends on 1) The parking facility mapping system of claim 1, wherein, the correction module further includes instructions to identify the parking block by executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in median orientation angles of a set of X estimate parking spaces within the same cluster being less than a predefined threshold, where X is greater than 1. ### Claim 4 (depends on 1) The parking facility mapping system of claim 1, wherein the correction module further includes instructions to identify the parking block by executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on each estimate parking space having at least one neighboring estimate parking space within the same cluster, where a neighboring estimate parking space has a lengthwise displacement less than a first threshold distance and a widthwise displacement less than a second threshold distance. ### Claim 5 (depends on 4) The parking facility mapping system of claim 4, wherein the first threshold distance is less than a median length of the estimate parking spaces that form the cluster of estimate parking spaces and the second threshold distance is greater than a median width of the estimate parking spaces that form the cluster of estimate parking spaces. ### Claim 6 (depends on 1) The parking facility mapping system of claim 1, wherein the instructions to determine the common geometric pattern among the cluster of estimate parking spaces comprise instructions to determine median values for size, shape and orientation of the estimate parking spaces that form the cluster of estimate parking spaces. ### Claim 7 (depends on 1) The parking facility mapping system of claim 1, wherein the instructions to identify the one or more gaps among the cluster of estimate parking spaces comprise: instructions to calculate a bounding box that encompasses the cluster of estimate parking spaces; and instructions to identify a space within the bounding box that has a width greater than a threshold amount. ### Claim 8 A method of creating a parking facility map, comprising: obtaining one or more images of a parking facility; generating an estimate map of parking spaces in the parking facility based at least on the one or more images, the estimate map of parking spaces including a plurality of estimate parking space shapes; identifying a parking block that includes a cluster of estimate parking spaces among the plurality of estimate parking space shapes; determining a common geometric pattern among the cluster of estimate parking spaces; correcting geometrical features of one or more estimate parking spaces in the cluster of estimate parking spaces based at least in part on the common geometric pattern; identifying one or more gaps among the cluster of estimate parking spaces; and determining a number of inserted parking spaces to fill the one or more gaps based at least in part on the common geometric pattern. ### Claim 9 (depends on 8) The method of claim 8, wherein the identifying the parking block comprises executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in orientation angles of estimate parking spaces within the same cluster being less than a predefined threshold. ### Claim 10 (depends on 8) The method of claim 8, wherein the identifying the parking block comprises executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in median orientation angles of a set of X estimate parking spaces within the same cluster being less than a predefined threshold, where X is greater than 1. ### Claim 11 (depends on 8) The method of claim 8, wherein the identifying the parking block comprises executing a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on each estimate parking space having at least one neighboring estimate parking space within the same cluster, where a neighboring estimate parking space has a lengthwise displacement less than a first threshold distance and a widthwise displacement less than a second threshold distance. ### Claim 12 (depends on 11) The method of claim 11, wherein the first threshold distance is less than a median length of the estimate parking spaces that form the cluster of estimate parking spaces and the second threshold distance is greater than a median width of the estimate parking spaces that form the cluster of estimate parking spaces. ### Claim 13 (depends on 8) The method of claim 8, wherein the determining the common geometric pattern among the cluster of estimate parking spaces comprises determining median values for size, shape and orientation of the estimate parking spaces that form the cluster of estimate parking spaces. ### Claim 14 (depends on 8) The method of claim 8, wherein the identifying one or more gaps among the cluster of estimate parking spaces comprises: calculating a bounding box that encompasses the cluster of estimate parking spaces; and identifying a space within the bounding box that has a width greater than a threshold amount. ### Claim 15 A non-transitory computer-readable medium for creating a parking facility map and including instructions that when executed by one or more processors cause the one or more processors to: obtain one or more images of a parking facility; generate an estimate map of parking spaces in the parking facility based at least on the one or more images, the estimate map of parking spaces including a plurality of estimate parking space shapes; identify a parking block that includes a cluster of estimate parking spaces among the plurality of estimate parking space shapes; determine a common geometric pattern among the cluster of estimate parking spaces; correct geometrical features of one or more estimate parking spaces in the cluster of estimate parking spaces based at least in part on the common geometric pattern; identify one or more gaps among the cluster of estimate parking spaces; and determine a number of inserted parking spaces to fill the one or more gaps based at least in part on the common geometric pattern. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the instructions to identify the parking block comprise instructions to execute a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in orientation angles of estimate parking spaces within the same cluster being less than a predefined threshold. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the instructions to identify the parking block comprise instructions to execute a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on a maximum difference in median orientation angles of a set of X estimate parking spaces within the same cluster being less than a predefined threshold, where X is greater than 1. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the instructions to identify the parking block comprise instructions to execute a clustering algorithm that classifies estimate parking spaces as being a part of a same cluster based at least in part on each estimate parking space having at least one neighboring estimate parking space within the same cluster, where a neighboring estimate parking space has a lengthwise displacement less than a first threshold distance and a widthwise displacement less than a second threshold distance. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the first threshold distance is less than a median length of the estimate parking spaces that form the cluster of estimate parking spaces and the second threshold distance is greater than a median width of the estimate parking spaces that form the cluster of estimate parking spaces. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the instructions to determine the common geometric pattern among the cluster of estimate parking spaces comprise instructions to determine median values for size, shape and orientation of the estimate parking spaces that form the cluster of estimate parking spaces.