Autonomous human-centric place recognition
First Claim
1. A computer-implemented method comprising:
- capturing sensor data using one or more sensors describing a particular environment;
processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the particular environment;
determining a location of the participant within the particular environment using depth image data, the depth image data describing at least a depth of points in the particular environment relative to the one or more sensors;
querying a feature database populated with a multiplicity of features extracted from the particular environment using the location of the participant for one or more features located within a search area defined by a search area dimension relative to the location of the participant, the one or more features representing one or more physical objects in the particular environment, the search area dimension including at least the depth based on the depth image data; and
selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature, of the one or more features located within the search area defined by the search area dimension relative to the location of the participant, being located within the scene type selected from among the plurality of predetermined scene types.
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Abstract
The novel technology described in this disclosure includes an example method comprising capturing sensor data using one or more sensors describing a particular environment; processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the environment; determining a location of the participant within the environment; querying a feature database populated with a multiplicity of features extracted from the environment using the location of the participant for one or more features being located proximate the location of the participant; and selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature of the one or more features being located within the scene types.
14 Citations
20 Claims
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1. A computer-implemented method comprising:
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capturing sensor data using one or more sensors describing a particular environment; processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the particular environment; determining a location of the participant within the particular environment using depth image data, the depth image data describing at least a depth of points in the particular environment relative to the one or more sensors; querying a feature database populated with a multiplicity of features extracted from the particular environment using the location of the participant for one or more features located within a search area defined by a search area dimension relative to the location of the participant, the one or more features representing one or more physical objects in the particular environment, the search area dimension including at least the depth based on the depth image data; and selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature, of the one or more features located within the search area defined by the search area dimension relative to the location of the participant, being located within the scene type selected from among the plurality of predetermined scene types. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 19)
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10. An autonomous computing system comprising:
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one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising; capturing sensor data using one or more sensors describing a particular environment, processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the particular environment, determining a location of the participant within the particular environment using depth image data, the depth image data describing at least a depth of points in the particular environment relative to the one or more sensors, querying a feature database populated with a multiplicity of features extracted from the particular environment using the location of the participant for one or more features located within a search area defined by a search area dimension relative to the location of the participant, the one or more features representing one or more physical objects in the particular environment, the search area dimension including at least the depth based on the depth image data, and selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature, of the one or more features located within the search area defined by the search area dimension relative to the location of the participant, being located within the scene type of the plurality of predetermined scene types. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 20)
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Specification