Model-based feature tracking system

Claim 1

A method for tracking a feature within data gathered by a sensor, the method comprising the steps of: developing an a priori model corresponding to a first location assessment point, which model contains data identifying the feature and an anticipated location of the feature; extracting feature location data from sensor data gathered at the first location assessment point; comparing the extracted feature location data with the anticipated feature location data; making a correction involving the sensor based on results of the comparison; building a new model with the feature location data extracted from the sensor data gathered at the first location assessment point; and at the first location assessment point, predicting the location of the feature in sensor data to be gathered at a second location assessment point using the new model.

Claim 2

A method for tracking a feature within data gathered by a sensor, the method comprising the steps of: developing an a priori model corresponding to a first location assessment point, which model contains data identifying the feature and an anticipated location of the feature; extracting feature location data from sensor data gathered at the first location assessment point; comparing the extracted feature location data with the anticipated feature location data; making a correction involving the sensor based on results of the comparison; building a new model with the feature location data extracted from the sensor data gathered at the first location assessment point; and at the first location assessment point, predicting the location of the feature in sensor data to be gathered at a second location assessment point using the new model.

Claim 8

A method for maintaining a point of reference as a sensor scans an object where one of the sensor and the object move relative to the other, the method comprising the steps of: sensing the object at a present assessment point to produce image data; extracting feature location data from the image data; prior to a second assessment point, using the feature location data to prepare a first model which contains information identifying a feature and an anticipated location of the feature in a subsequent image associated with the second assessment point; comparing the anticipated feature location data in the first model to feature location data extracted from the subsequent image; determining when a match occurs between the anticipated feature location data in the first model and the feature location data extracted from the subsequent image; correcting a sensor orientation based on conditions corresponding to when a match occurs between the model data and the subsequent image data; and replacing the first model with a new model, wherein the new model is constructed using the feature location data extracted from the subsequent image.

Claim 9

A method for maintaining a point of reference as a sensor scans an object where one of the sensor and the object move relative to the other, the method comprising the steps of: sensing the object at a present assessment point to produce image data; extracting feature location data from the image data; prior to a second assessment point, using the feature location data to prepare a first model which contains information identifying a feature and an anticipated location of the feature in a subsequent image associated with the second assessment point; comparing the anticipated feature location data in the first model to feature location data extracted from the subsequent image; determining when a match occurs between the anticipated feature location data in the first model and the feature location data extracted from the subsequent image; correcting a sensor orientation based on conditions corresponding to when a match occurs between the model data and the subsequent image data; and replacing the first model with a new model, wherein the new model is constructed using the feature location data extracted from the subsequent image.

Claim 11

A method for tracking a feature within sensor data gathered along a succession of location assessment points, the method comprising the steps of: acquiring a priori data containing the feature; extracting first feature location data from the a priori data; building a first predictive model, based on the first feature location data, which first predictive model contains data identifying the feature and an anticipated location of the feature in sensor data to be gathered at a first location assessment point; acquiring a plural number of sensor data sets at the first location assessment point, each of the plural sensor data sets corresponding to a sensor orientation in a sensor scan pattern; extracting feature identification and location data from each of the plural sensor data sets and comparing the extracted feature identification and location data from each data set with the feature identification data and anticipated feature location data associated with the first predictive model until a match is found; using a sensor orientation corresponding to the match to align the sensor; and at the first location assessment point, preparing a second predictive model with the extracted feature identification and location data corresponding to the match, wherein the second predictive model contains data identifying the feature and an anticipated location of the feature at a second location assessment point.

Claim 12

A method for tracking a feature within sensor data gathered along a succession of location assessment points, the method comprising the steps of: acquiring a priori data containing the feature; extracting first feature location data from the a priori data; building a first predictive model, based on the first feature location data, which first predictive model contains data identifying the feature and an anticipated location of the feature in sensor data to be gathered at a first location assessment point; acquiring a plural number of sensor data sets at the first location assessment point, each of the plural sensor data sets corresponding to a sensor orientation in a sensor scan pattern; extracting feature identification and location data from each of the plural sensor data sets and comparing the extracted feature identification and location data from each data set with the feature identification data and anticipated feature location data associated with the first predictive model until a match is found; using a sensor orientation corresponding to the match to align the sensor; and at the first location assessment point, preparing a second predictive model with the extracted feature identification and location data corresponding to the match, wherein the second predictive model contains data identifying the feature and an anticipated location of the feature at a second location assessment point.

Claim 15

A method for providing correction for a sensor in a sensor platform which gathers sensor data at successive position assessment points, the method comprising the steps of: forming a succession of models each corresponding to one of the position assessment points, wherein each model contains data for predicting the location of features in sensor data to be gathered at a subsequent position assessment point, each model being formed prior to the subsequent position assessment point based on sensor data exclusively associated with a current position assessment point; acquiring a sensor data set at each position assessment point; extracting sensor feature location data from each sensor data set; comparing extracted sensor feature location data and model feature location data to determine a difference between a location of the feature; and aligning the sensor in accordance with the difference.

Claim 16

A method for providing correction for a sensor in a sensor platform which gathers sensor data at successive position assessment points, the method comprising the steps of: forming a succession of models each corresponding to one of the position assessment points, wherein each model contains data for predicting the location of features in sensor data to be gathered at a subsequent position assessment point, each model being formed prior to the subsequent position assessment point based on sensor data exclusively associated with a current position assessment point; acquiring a sensor data set at each position assessment point; extracting sensor feature location data from each sensor data set; comparing extracted sensor feature location data and model feature location data to determine a difference between a location of the feature; and aligning the sensor in accordance with the difference.

Claim 17

A method for tracking a feature appearing in successive sensor data sets acquired by a sensor, the method comprising the steps of: extracting feature location data from a priori information; forming a first predictive model based on the extracted feature location data, which first predictive model provides anticipated feature location data within a first position assessment area; acquiring a plural number of first sensor data sets in the first position assessment area; extracting feature location data from each sensor data set comparing the extracted feature location data to the anticipated feature location data until a match is found; correcting the sensor based on conditions corresponding to the match; and at the first position assessment area, forming a second predictive model from the extracted feature location data corresponding to the match, wherein the second predictive model provides anticipated feature location data for a second position assessment area.

Claim 18

A method for tracking a feature appearing in successive sensor data sets acquired by a sensor, the method comprising the steps of: extracting feature location data from a priori information; forming a first predictive model based on the extracted feature location data, which first predictive model provides anticipated feature location data within a first position assessment area; acquiring a plural number of first sensor data sets in the first position assessment area; extracting feature location data from each sensor data set comparing the extracted feature location data to the anticipated feature location data until a match is found; correcting the sensor based on conditions corresponding to the match; and at the first position assessment area, forming a second predictive model from the extracted feature location data corresponding to the match, wherein the second predictive model provides anticipated feature location data for a second position assessment area.

Claim 20

An apparatus for maintaining a correct point of reference as a sensor scans an object where one of the sensor and the object move relative to the other, the apparatus comprising: means for sensing the object at a first assessment point to produce image data; means for extracting location data for a feature from the image data; means for using the feature location data prior to a second assessment point to prepare a model which contains information identifying the feature and an anticipated location of the feature in a first subsequent image associated with the second assessment point; means for comparing the anticipated location with feature location data extracted from the first subsequent image; means for determining a match between the anticipated location of the feature and the feature location data extracted from the first subsequent image; means for performing a correction involving the sensor based on conditions corresponding to the match ; and at the second assessment point, means for using the feature location data extracted from the first subsequent image to prepare a new model which contains information regarding an anticipated location of the feature in a second subsequent image.

Claim 21

An apparatus for maintaining a correct point of reference as a sensor scans an object where one of the sensor and the object move relative to the other, the apparatus comprising: means for sensing the object at a first assessment point to produce image data; means for extracting location data for a feature from the image data; means for using the feature location data prior to a second assessment point to prepare a model which contains information identifying the feature and an anticipated location of the feature in a first subsequent image associated with the second assessment point; means for comparing the anticipated location with feature location data extracted from the first subsequent image; means for determining a match between the anticipated location of the feature and the feature location data extracted from the first subsequent image; means for performing a correction involving the sensor based on conditions corresponding to the match ; and at the second assessment point, means for using the feature location data extracted from the first subsequent image to prepare a new model which contains information regarding an anticipated location of the feature in a second subsequent image.

Claim 28

An apparatus for tracking a feature appearing in data acquired by a sensor, the apparatus comprising: at a present feature location assessment point, means for preparing a model for predicting a location of the feature in sensor data to be gathered at a future location assessment point, wherein the model is based on feature location data exclusively associated with the present feature location assessment point; means for extracting feature location data from sensor data gathered at the future location assessment point; means for comparing the predicted feature location from the model with the feature location data extracted from the sensor data gathered at the future location assessment point; means for determining when the comparison results in a match; means for deriving sensor conditions corresponding to the match; and means for correcting a sensor orientation based on the conditions corresponding to the match.

Claim 29

An apparatus for tracking a feature appearing in data acquired by a sensor, the apparatus comprising: at a present feature location assessment point, means for preparing a model for predicting a location of the feature in sensor data to be gathered at a future location assessment point, wherein the model is based on feature location data exclusively associated with the present feature location assessment point; means for extracting feature location data from sensor data gathered at the future location assessment point; means for comparing the predicted feature location from the model with the feature location data extracted from the sensor data gathered at the future location assessment point; means for determining when the comparison results in a match; means for deriving sensor conditions corresponding to the match; and means for correcting a sensor orientation based on the conditions corresponding to the match.