Abstract: A method includes receiving a trajectory dataset including a plurality of geospatial points forming a point cloud and acquired along a trajectory wherein for each of the plurality of geospatial points there is a defined an x-coordinate, a y-coordinate and a z-coordinate and at least one mapping device orientation attribute, segmenting the trajectory dataset into a plurality of segments, determining at least one relative constraint for each of the plurality of segments and utilizing, for each of the plurality of segments, at least one of the determined relative constraints to determine a relative position of at least two of the plurality of segments.

Abstract: A method includes receiving a trajectory dataset including a plurality of geospatial points forming a point cloud and acquired along a trajectory wherein for each of the plurality of geospatial points there is a defined an x-coordinate, a y-coordinate and a z-coordinate and at least one mapping device orientation attribute, segmenting the trajectory dataset into a plurality of segments, determining at least one relative constraint for each of the plurality of segments and utilizing, for each of the plurality of segments, at least one of the determined relative constraints to determine a relative position of at least two of the plurality of segments.

Abstract: A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Abstract: A system configured to derive a motion estimate for a SLAM device using an IMU forming a part of the SLAM system. The system may be configured to refine the motion estimate via a visual-inertial odometry optimization process to produce a refined estimate and refine the refined estimate via a laser odometry optimization process by minimizing at least one residual squared error between at least one feature in a current scan and at least one previously scanned feature.

Abstract: A method comprises accessing a data set comprising a LIDAR acquired point cloud comprising a plurality of points each of which are attributed with at least a geospatial coordinate, sub-sampling at least a portion of the plurality of points to derive a representative sample of the plurality of points and displaying the representative sample of the plurality of points.

Abstract: A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Abstract: A method comprises accessing a data set comprising a LIDAR acquired point cloud comprising a plurality of points each of which are attributed with at least a geospatial coordinate, sub-sampling at least a portion of the plurality of points to derive a representative sample of the plurality of points and displaying the representative sample of the plurality of points.

Abstract: A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Abstract: A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Abstract: A method includes acquiring with a scanning device a scan frame comprising a point cloud comprising a first plurality of points, attributing each of the first plurality of points with a selected metric, filtering an existing map comprised of a second plurality of points based, at least in part, on the selected metric and localizing each of the first plurality of points to the filtered second plurality of points.

Abstract: A method includes receiving a trajectory dataset including a plurality of geospatial points forming a point cloud and acquired along a trajectory wherein for each of the plurality of geospatial points there is a defined an x-coordinate, a y-coordinate and a z-coordinate and at least one mapping device orientation attribute, segmenting the trajectory dataset into a plurality of segments, determining at least one relative constraint for each of the plurality of segments and utilizing, for each of the plurality of segments, at least one of the determined relative constraints to determine a relative position of at least two of the plurality of segments.

Abstract: A method includes retrieving a map of a 3D geometry of an environment the map including a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.

Abstract: A method includes receiving data from an IMU device at a first computational module at a first frequency and computing, based at least in part on the received IMU data, a first estimated position of a mobile mapping system, receiving the first estimated position and visual-inertial data at a second computational module at a second frequency and computing, based at least in part on the first estimated position and visual-inertial data, a second estimated position of the mobile mapping system and receiving the second estimated position and laser scan data at a third computational module at a third frequency and computing, based at least in part on the second estimated position and laser scan data, a third estimated position of the mobile mapping system.

Abstract: A method comprises accessing a data set comprising a LIDAR acquired point cloud comprising a plurality of points each of which are attributed with at least a geospatial coordinate, sub-sampling at least a portion of the plurality of points to derive a representative sample of the plurality of points and displaying the representative sample of the plurality of points.