Kaarta, Inc.

Carnegie Mellon University

Near Earth Autonomy, Inc.

Ji Zhang

Kevin Joseph Dowling

Sanjiv Singh

Calvin Wade Sheen

Steven Huber

Ethan Abramson

Brian Boyle

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.

Laser scanner with real-time, online ego-motion estimation

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.

Methods and systems for geo-referencing mapping systems

A mapping system, comprising an inertial measurement unit; a camera unit; a laser scanning unit; and a computing system in communication with the inertial measurement unit, the camera unit, and the laser scanning unit, wherein the computing system computes first measurement predictions based on inertial measurement data from the inertial measurement unit at a first frequency, second measurement predictions based on the first measurement predictions and visual measurement data from the camera unit at a second frequency and third measurement predictions based on the second measurement predictions and laser ranging data from the laser scanning unit at a third frequency.

A method of pose calculation for a portable three-dimensional scanning device including a first sensor and a second sensor the method including utilizing data from the first sensor and data from the second sensor to acquire data defining six degrees of freedom of the scanning device to optimize a first pose calculation, receiving data comprising one of data from the first sensor and data from the second sensor, selecting a subset of the six degrees of freedom of the scanning device, utilizing the data from the first sensor and the received data for the selected subset of six degrees of freedom to optimize a second pose, wherein the unselected degrees of freedom are retained from the first pose and storing received data associated with the second camera pose in a point cloud database.

Systems and methods for improvements in scanning and mapping

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.

A system configured to autonomously operate a vehicle within an environment is disclosed herein. The system includes a vehicle including a sensing system with a single active sensor configured to detect objects within an environment as the vehicle travels on a journey along a travel path within the environment. The system further includes a computing system communicably coupled to the vehicle. The computing system includes a memory configured to store a three-dimensional map of the environment, and a processor configured to determine an updated pose of the vehicle based on the three-dimensional map and input from the single active sensor of the vehicle. The processor is further configured to generate an updated travel path for the vehicle, wherein the updated travel path is generated based on the updated pose of the vehicle within the environment determined by the computing system.

Limited-sensor 3D localization system for mobile vehicle

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.

Aligning measured signal data with SLAM localization data and uses thereof

A method includes determining a mesh representing an environment based, at least in part, on a point cloud the mesh comprising a plurality of triangles each having a normal vector, acquiring one or more images of the environment wherein each of the one or more images is attributed with a view position and a view angle and applying the one or more images to the mesh.

Growing community of inventors

Pittsburgh, PA, United States of America

Ji Zhang