Method for increasing waypoint accuracies for crowd-sourced routes
First Claim
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1. A method for increasing route accuracies, the method comprising:
- recording a waypoint while moving along a route;
recording a positional-accuracy measurement for the recorded waypoint;
calculating a position accuracy prediction (PAP) parameter for the recorded waypoint based on the recorded positional-accuracy measurement wherein the PAP parameter comprises a first weight value, a first confidence value, and an-a first error distance; and
comparing, with a computer processor, the recorded waypoint and the calculated PAP parameter to a corpus waypoint and an associated corpus PAP parameter stored on a computer-based system, wherein the corpus PAP parameter comprises a second weight value, a second confidence value, and a second error distance, and wherein the comparing step comprises;
calculating a weight average based on the first and second weight values;
computationally extending the first error distance from the PAP parameter of the recorded waypoint to provide a first probability distribution circle;
computationally extending the second error distance from the PAP parameter of the corpus waypoint to provide a second probability distribution circle; and
computationally generating a vector line between the recorded waypoint and the corpus waypoint; and
determining, with the computer processor, an updated waypoint along the vector line between the recorded waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average.
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Abstract
A method for increasing route accuracies, comprising receiving GPS waypoints associated position accuracy prediction (PAP) parameters, comparing the PAP parameters to corpus PAP parameters associated with corpus GPS waypoints, and updating the corpus PAP parameters and the corpus GPS waypoints based on the comparison of the PAP parameters to the corpus PAP parameters.
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Citations
17 Claims
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1. A method for increasing route accuracies, the method comprising:
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recording a waypoint while moving along a route; recording a positional-accuracy measurement for the recorded waypoint; calculating a position accuracy prediction (PAP) parameter for the recorded waypoint based on the recorded positional-accuracy measurement wherein the PAP parameter comprises a first weight value, a first confidence value, and an-a first error distance; and comparing, with a computer processor, the recorded waypoint and the calculated PAP parameter to a corpus waypoint and an associated corpus PAP parameter stored on a computer-based system, wherein the corpus PAP parameter comprises a second weight value, a second confidence value, and a second error distance, and wherein the comparing step comprises; calculating a weight average based on the first and second weight values; computationally extending the first error distance from the PAP parameter of the recorded waypoint to provide a first probability distribution circle; computationally extending the second error distance from the PAP parameter of the corpus waypoint to provide a second probability distribution circle; and computationally generating a vector line between the recorded waypoint and the corpus waypoint; and determining, with the computer processor, an updated waypoint along the vector line between the recorded waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for increasing route accuracies, the method comprising:
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receiving a first GPS waypoint and a first position accuracy prediction (PAP) parameter associated with the first GPS waypoint, wherein the first PAP parameter comprises a first quality parameter set having a first weight value, a first confidence value for the first GPS waypoint, and a first error distance for the first GPS waypoint; comparing, with a computer processor, the first PAP parameter to a corpus PAP parameter associated with a corpus GPS waypoint, wherein the corpus PAP parameter comprises a second quality parameter set having a second weight value, a second confidence value for the corpus GPS waypoint, and a second error distance for the corpus GPS waypoint, wherein the comparing step comprises; calculating a weight average based on the first and second weight values; computationally extending the first error distance from the PAP parameter of the first GPS waypoint to provide a first probability distribution circle; computationally extending the second error distance from the PAP parameter of the corpus GPS waypoint to provide a second probability distribution circle; and computationally generating a vector line between the first GPS waypoint and the corpus GPS waypoint; and determining, with the computer processor, an updated GPS waypoint along the vector line between the first GPS waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A method for increasing route accuracies, the method comprising:
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receiving a first GPS waypoint and a first position accuracy prediction (PAP) parameter, wherein the first PAP parameter comprises a first quality parameter set having a first weight value, a first confidence value for the first GPS waypoint, and a first error distance for the first GPS waypoint; reading, with a computer processor, corpus route data from a computer readable medium, the corpus route data comprising a corpus GPS waypoint and an associated corpus PAP parameter, wherein the corpus PAP parameter comprises a second quality parameter set having a second weight value, a second confidence value for the corpus GPS waypoint, and a second error distance for the corpus GPS waypoint; calculating a weight average based on a ratio of the first weight value of the first quality parameter set and the second weight value of the second quality parameter set; computationally extending the first error distance from the PAP parameter of the first GPS waypoint to provide a first probability distribution circle; computationally extending the second error distance from the PAP parameter of the corpus GPS waypoint to provide a second probability distribution circle; and computationally generating a vector line between the first GPS waypoint and the corpus GPS waypoint; determining, with the computer processor, an updated GPS waypoint along the vector line between the first GPS waypoint and the corpus waypoint and within an overlapping area of the first and second probability distribution circles based on the calculated weight average; replacing the corpus GPS waypoint with the updated GPS waypoint; adding the first weight value and the second weight value to attain a summed weight value; and
replacing the second weight value in the second quality parameter set with the summed weight value. - View Dependent Claims (15, 16, 17)
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Specification