Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: A translatable assist handle system for a vehicle includes a guide track having a first end, a second end, and an intermediate portion having a guide surface that extends around at least a portion of a vehicle opening between the first end and the second end. A translatable assist handle including a guide element is disposed in the guide track. The translatable assist handle is selectively positionable along the guide track providing an adjustable hand hold aiding a passenger to ingress and egress from the vehicle.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: The present invention relates to an assembly apparatus for assembling components of spacecraft in space. The assembly apparatus includes: a core platform; and a mobile platform including an end effector configured to perform an assembly or manufacturing task. The mobile platform is connected to the core platform by a tether. The core platform includes a body and a coupling element connected to and extendable from the body such that the coupling element may be spaced from the body of the core platform. The tether connects the mobile platform to the body via the coupling element. The assembly apparatus further includes an actuator configured to vary the length of the tether extending between the coupling element and the mobile platform to control the position of the mobile platform relative to the body of the core platform.

Abstract: In one embodiment, a device includes a processor to apply a hash function to values in an index column of a database table yielding hash values defining a plurality of buckets of a hash table, copy data from rows of the database table into the hash table as a plurality of tuples, each tuple including a respective key from the index column of the database table, or the respective key from the index column of the database table and at least one value from a row of the database table including the respective key, arrange the plurality of tuples among the buckets in the hash table, a given bucket of the plurality of buckets including multiple tuples of the plurality of tuples, and store the multiple tuples of the given bucket back-to-back without any pointer between the tuples to find at least one next tuple of the multiple tuples.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: A translatable assist handle system for a vehicle includes a guide track having a first end, a second end, and an intermediate portion having a guide surface that extends around at least a portion of a vehicle opening between the first end and the second end. A translatable assist handle including a guide element is disposed in the guide track. The translatable assist handle is selectively positionable along the guide track providing an adjustable hand hold aiding a passenger to ingress and egress from the vehicle.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: The present invention relates to an assembly apparatus for assembling components of spacecraft in space. The assembly apparatus comprises: a core platform (7); and a mobile platform (4) comprising an end effector configured to perform an assembly or manufacturing task. The mobile platform (4) is connected to the core platform (7) by a tether (6a). The core platform comprises a body and a coupling element (15a) connected to and extendable from the body (7) such that the coupling element (15a) may be spaced from the body of the core platform (7). The tether (6a) connects the mobile platform (4) to the body (7) via the coupling element (15a). The assembly apparatus further comprises an actuator configured to vary the length of the tether extending between the coupling element and the mobile platform to control the position of the mobile platform relative to the body of the core platform.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

Abstract: This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.