Compaq Computer Corporation, Inc.

Caterpillar Inc.

Carnegie Mellon University

Daniel Dale Morris

James Matthew Rehg

Hong Chang

Bradley Scott Kriel

Paul Russell Friend

Lawrence Thomas Pileggi

Qi Chen

Jodi Graf

A motion determination system is disclosed. The system may receive a first and a second camera image from a camera, the first camera image received earlier than the second camera image. The system may identify corresponding features in the first and second camera images. The system may receive range data comprising at least one of a first and a second range data from a range detection unit, corresponding to the first and second camera images, respectively. The system may determine first positions and the second positions of the corresponding features using the first camera image and the second camera image. The first positions or the second positions may be determined by also using the range data. The system may determine a change in position of the machine based on differences between the first and second positions, and a VO-based velocity of the machine based on the determined change in position.

Motion estimation utilizing range detection-enhanced visual odometry

An image processing system has a plurality of cameras and a display that are mounted on a machine. The plurality of cameras are configured to generate image data for an environment of the machine. The image processing system also has a processor connected to the plurality of cameras and the display. The processor is configured to access the image data from the plurality of cameras, access parameters associated with the plurality of cameras, generate a unified image by combining the image data from the plurality of cameras based at least in part on the parameters, access state data associated with the machine, select a portion of the unified image based at least in part on the state data, and render the portion of the unified image on the display.

Image processing system using unified images

A object recognition system has a camera configured to generate image data of an environment of a machine and a radar device configured to generate radar data of the environment of the machine. The object recognition system also has a processor configured to detect an object in the environment based on the radar data, map the radar data to a portion of the image data corresponding to the detected object, and classify the detected object using the mapped portion of the image data.

Object recognition system having radar and camera input

An integrated circuit comprising both memory and logic wherein at least one layer of the integrated circuit is fabricated using a common grating pattern for both memory and logic is described. In one embodiment, the integrated circuit comprises a substrate, an active layer, and a gate material layer such as a polysilicon layer, and the active layer, the gate material layer, or both the active layer and the gate material layer are formed using a common grating pattern for both memory and logic. By using a common grating pattern for both memory and logic, a corresponding layer of the integrated circuit can be reliably and affordably manufactured using sub-wavelength lithography.

Regular pattern arrays for memory and logic on a semiconductor substrate

In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.

Method for tracking the motion of a 3-D figure

A computerized method compresses an input sequence of 2-D images captured by a monocular camera. The input sequence of images includes a moving articulated 3-D figure. A pose of the figure in each image is registered using a 2-D scaled prismatic model to produce a state trajectory for the figure in each image of the input sequence. The state trajectory of the figure in the images is reconstructed to determine kinematic parameters of a 3-D kinematic model that best fit the state trajectories, The 3-D kinematic model represents the 3-D movement of the figure in the input sequence of 2-D images. Static information of a first image of the input sequence is encoded. For subsequent images of the input sequence, a time series of state vectors containing the kinematic parameters of the moving figure are encoded. The static information of the first image and the time series of state vectors can be used to generate an output sequence of images including the moving articulated 3-D figure on a viewing device.

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Pittsburgh, PA, United States of America

Daniel Dale Morris