Intel Corporation

Avinash Kumar

Sridhar G Sharma

Ramkumar Narayanswamy

Nilesh Amar Ahuja

Manjula Gururaj

Ignacio Javier Alvarez

Richmond Hicks

S M Iftekharul Alam

Jason Martin

Sameer Sheorey

Abel Shin

Michael Schmidt

Techniques related to calibration of wide-baseline outside-in multi camera systems are discussed. Such techniques include receiving video sequences and intrinsic parameters corresponding to each of the cameras in the system, generating first extrinsic parameters for the cameras using tracked features and the intrinsic parameters and second extrinsic parameters using location parameters corresponding to motion control systems coupled to the cameras, and combining the first and second extrinsic parameters to provide real-time calibration of extrinsic parameters for the cameras.

Real-time calibration of wide-baseline outside-in multi-camera systems

Disclosures herein may be directed to a method, technique, or apparatus directed to a computer-assisted or autonomous driving (CA/AD) vehicle that includes a system controller, disposed in a first CA/AD vehicle, to manage a collaborative three-dimensional (3-D) map of an environment around the first CA/AD vehicle, wherein the system controller is to receive, from another CA/AD vehicle proximate to the first CA/AD vehicle, an indication of at least a portion of another 3-D map of another environment around both the first CA/AD vehicle and the another CA/AD vehicle and incorporate the at least the portion of the 3-D map proximate to the first CA/AD vehicle and the another CA/AD vehicle into the 3-D map of the environment of the first CA/AD vehicle managed by the system controller.

Collaborative 3-D environment map for computer-assisted or autonomous driving vehicles

Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to detect an anomalous image associated with image data from one or more cameras of a computer-aided or autonomous driving (CA/AD) vehicle. Embodiments may include a sensor interface disposed in the CA/AD vehicle to receive, from the one or more cameras, a stream of image data including single view image data captured by the one or more cameras or multi-view image data collaboratively captured by multiple ones of the one or more cameras. In embodiments, a consistency analysis unit disposed in the CA/AD vehicle is coupled to the sensor interface to perform a consistency check on pixel-level data using single view or multi-view geometric methods to determine whether the image data includes an anomalous image. Other embodiments may also be described and claimed.

Detection of an anomalous image associated with image data from one or more cameras of a computer-aided or autonomous driving vehicle

An embodiment of a semiconductor package apparatus may include technology to capture two or more concurrent images of a scene with two or more cameras, detect a feature in a first image from a first camera of the two or more cameras, match the feature in a second image from a second camera of the two or more cameras, and perform a photometric calibration between the first camera and the second camera based on a portion of the first image corresponding to the detected feature and a portion of the second image corresponding to the matched feature. Other embodiments are disclosed and claimed.

Multi-camera processor with feature matching

System, apparatus, method, and computer readable media for on-the-fly dynamic calibration of multi-camera platforms using images of multiple different scenes. Image frame sets previously captured by the platform are scored as potential candidates from which new calibration parameters may be computed. The candidate frames are ranked according to their score and iteratively added to the calibration frame set according to an objective function. The selected frame set may be selected from the candidates based on a reference frame, which may be a most recently captured frame, for example. A device platform including a CM and comporting with the exemplary architecture may enhance multi-camera functionality in the field by keeping calibration parameters current. Various computer vision algorithms may then rely upon these parameters, for example.

Dynamic calibration of multi-camera systems using multiple multi-view image frames

An example apparatus for calibrating depth cameras includes an image receiver to receive an image from a depth camera. The apparatus also includes an object detector to detect a natural object with an expected shape in the image. The apparatus further includes a parameter adjuster to adjust a parameter of the depth camera to reduce a detected difference between the expected shape and a measured characteristic of the natural object.

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Santa Clara, CA, United States of America

Avinash Kumar