Methods for simultaneous localization and mapping (SLAM) and related apparatus and systems
First Claim
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1. A method of estimating a location of a mobile device in a two-dimensional (2D) or three-dimensional (3D) space, the method comprising:
- obtaining a first map comprising coordinates of a plurality of first features within a first coordinate space and respective first regions of uncertainty of the coordinates of each of the first features, wherein the first regions of uncertainty include at least two regions with non-proportional dimensions;
obtaining a second map comprising coordinates of a plurality of second features within a second coordinate space and respective second regions of uncertainty for the coordinates of each of the second features;
determining a plurality of feature pairs, wherein each feature pair includes a first feature of the first map and a second feature of the second map;
performing, with one or more data processing apparatus, one or more iterations of an iterative process, including;
(a) determining third regions of uncertainty of the coordinates of the respective first features,(b) determining a potential transformation between the first coordinate space and the second coordinate space,(c) determining probabilities of the feature pairs based, at least in part, on the third regions of uncertainty, wherein the probability of each feature pair is a probability that the coordinates of the first feature of the feature pair represent a measurement of the second feature of the feature pair obtained from a potential location of the mobile device corresponding to the potential transformation,(d) determining a value representative of a statistical optimality of the potential transformation by evaluating an objective function, wherein the objective function aggregates the probabilities of the feature pairs,(e) determining whether the value of the objective function has reached a local extreme value of the objective function, and(f) terminating the iterative process if the value of the objective function has reached the local extreme value, otherwise performing another iteration of the iterative process,wherein for at least one of the iterations, the third regions of uncertainty are determined based, at least in part, on the first regions of uncertainty, and the probabilities of the feature pairs are determined based, at least in part, on the first regions of uncertainty with the non-proportional dimensions; and
estimating the location of the mobile device based on the potential transformation corresponding to the terminating of the iterative process.
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Abstract
Some embodiments of location estimation methods may (1) facilitate the task of efficiently finding the location of a mobile platform in scenarios in which the uncertainties associated with the coordinates of the map features are anisotropic and/or non-proportional, and/or (2) facilitate decoupling of location estimation from feature estimation. Some embodiments of feature estimation methods may (1) facilitate the combining of environmental descriptions provided by two or more mobile platforms, and/or (2) facilitate decoupling of a data aggregation from feature re-estimation.
91 Citations
18 Claims
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1. A method of estimating a location of a mobile device in a two-dimensional (2D) or three-dimensional (3D) space, the method comprising:
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obtaining a first map comprising coordinates of a plurality of first features within a first coordinate space and respective first regions of uncertainty of the coordinates of each of the first features, wherein the first regions of uncertainty include at least two regions with non-proportional dimensions; obtaining a second map comprising coordinates of a plurality of second features within a second coordinate space and respective second regions of uncertainty for the coordinates of each of the second features; determining a plurality of feature pairs, wherein each feature pair includes a first feature of the first map and a second feature of the second map; performing, with one or more data processing apparatus, one or more iterations of an iterative process, including; (a) determining third regions of uncertainty of the coordinates of the respective first features, (b) determining a potential transformation between the first coordinate space and the second coordinate space, (c) determining probabilities of the feature pairs based, at least in part, on the third regions of uncertainty, wherein the probability of each feature pair is a probability that the coordinates of the first feature of the feature pair represent a measurement of the second feature of the feature pair obtained from a potential location of the mobile device corresponding to the potential transformation, (d) determining a value representative of a statistical optimality of the potential transformation by evaluating an objective function, wherein the objective function aggregates the probabilities of the feature pairs, (e) determining whether the value of the objective function has reached a local extreme value of the objective function, and (f) terminating the iterative process if the value of the objective function has reached the local extreme value, otherwise performing another iteration of the iterative process, wherein for at least one of the iterations, the third regions of uncertainty are determined based, at least in part, on the first regions of uncertainty, and the probabilities of the feature pairs are determined based, at least in part, on the first regions of uncertainty with the non-proportional dimensions; and estimating the location of the mobile device based on the potential transformation corresponding to the terminating of the iterative process. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A method of estimating a location of a mobile device in a two-dimensional (2D) or three-dimensional (3D) space, the method comprising:
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obtaining a first map comprising coordinates of a plurality of first features within a first coordinate space and first data characterizing first uncertainties associated with the coordinates of the first features; obtaining a second map comprising coordinates of a plurality of second features within a second coordinate space and second data characterizing second uncertainties associated with the coordinates of the second features; determining a plurality of feature pairs, wherein each feature pair includes a first feature of the first map and a second feature of the second map; performing, with one or more data processing apparatus, one or more iterations of an iterative process, including; (a) determining third data characterizing third uncertainties associated with the coordinates of the first features, (b) determining a potential transformation between the first coordinate space and the second coordinate space, (c) determining probabilities of the feature pairs based, at least in part, on the third uncertainties, wherein the probability of each feature pair is a probability that the coordinates of the first feature of the feature pair represent a measurement of the second feature of the feature pair obtained from a potential location of the mobile device corresponding to the potential transformation, the probability of each feature pair is determined based, at least in part, on a weight associated with the feature pair, the weight associated with each feature pair is determined based, at least in part, on relationship between a pull of the feature pair and a distribution of pulls of the plurality of feature pairs, and the pull of each feature pair comprises a product of a residual of the feature pair and an inverse square root of an uncertainty covariance of the feature pair, (d) determining a value representative of a statistical optimality of the potential transformation by evaluating an objective function, wherein the objective function aggregates the probabilities of the feature pairs, (e) determining whether the value of the objective function has reached a local extreme value of the objective function, and (f) terminating the iterative process if the third uncertainties are equal to the first uncertainties, a value of a stabilized covariance of the pull distribution is less than a threshold value on each axis of the pull distribution, and the value of the objective function has reached the local extreme value, otherwise performing another iteration of the iterative process; and estimating the location of the mobile device based on the potential transformation corresponding to the terminating of the iterative process. - View Dependent Claims (18)
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Specification