Connaught Electronics Limited

Bayerische Motoren Werke Aktiengesellschaft

Application Solutions (electronics and Vision) Ltd.

Pantelis Ermilios

Myles Friel

Bryan Mcnamara

William O'Grady

Ciaran Hughes

Guenter Bauer

Jonathan Horgan

Sunil Chandra

Dave Wibberley

Peter Bone

Matthias Lenk

Peter Gagnon

Ciáran Hughes

A method for forming a point cloud corresponding to the topography of an imaged environment involves acquiring an image of the environment with a camera having a WFOV lens mounted on a vehicle; changing the camera pose by an adjustment greater than a threshold; and acquiring another image of the environment with the camera at the changed pose. The images are mapped onto respective surfaces to form respective mapped images, defined by the same nonplanar geometry. One of the mapped images is divided into blocks of pixels. For each block, a depth map is formed by performing a search through the other mapped image to evaluate a disparity in the position of the location of the block of pixels in each of the mapped images. The depth map is converted into a partial point cloud corresponding to the local topography of the environment surrounding the vehicle as it moves through the environment.

Method for measuring the topography of an environment

The invention relates to a method for recognizing a raised object on the basis of images in an environmental region of a motor vehicle, comprising: capturing a first image from a first camera and a second image from a second camera, transforming the first and second images into a common reference system, forming gradients over pixel values of pixels along gradient lines in the transformed first and second images, summing gradients along parallel summation lines to form a gradient sum, determining a first pair of the maxima of the gradient sum in the transformed first image and a second pair of the maxima of the gradient sum in the transformed second image, recognizing the raised object, if at least one distance between the maxima of the first pair in the transformed first image deviates from a distance of the maxima of the corresponding second pair in the transformed second image.

Recognizing a raised object on the basis of perspective images

The invention relates to a method for detecting a rolling shutter effect in images of an environmental region () of a motor vehicle () captured by an image sensor of at least one camera () of the motor vehicle () comprising a plurality of sensor lines (), including the following steps: a) determining a first position (P) on a sensor plane () of the image sensor corresponding to a feature () when capturing a first image and identifying a second position (P) on the sensor plane () corresponding to the feature () when capturing a second image, b) determining a motion vector (v) characterizing a displacement of the feature () on the sensor plane () between the first position (P) and the second position (P), c) determining a vertical span (&#x394;y) of the motion vector (v) characterizing a number of sensor lines () at least partially covered by the motion vector (v), d) detecting the rolling shutter effect based on the vertical span (&#x394;y). I addition, the invention relates to a computing device (), a driver assistance system () as well as a motor vehicle ().

Method for detecting a rolling shutter effect in images of an environmental region of a motor vehicle, computing device, driver assistance system as well as motor vehicle

The invention relates to a method for detecting an object () alongside a road () of a motor vehicle () based on at least two images () of an environmental region () of the motor vehicle () consecutively captured by at least one vehicle-side camera () for extrinsic calibration of the at least one camera (), wherein the images () at least partially display a texture of a road surface () and wherein the following steps are performed: a) determining at least two mutually corresponding blocks () based on the at least two images (); b) determining respective motion vectors for each of the at least two pairs of mutually corresponding blocks (); c) determining a depth information concerning the at least two images () based on the at least two motion vectors; d) detecting the object () based on the depth information. The invention also relates to a computing device (), a driver assistance system () as well as a motor vehicle ().

Method and system for detecting an object alongside a road of a motor vehicle based on at least two images of an environmental region of the motor vehicle

The invention relates to a method for motion estimation between two images of an environmental region () of a motor vehicle () captured by a camera () of the motor vehicle (), wherein the following steps are performed: a) determining at least two image areas of a first image as at least two first blocks (B) in the first image, b) for each first block (B), defining a respective search region in a second image for searching the respective search region in the second image for a second block (B) corresponding to the respective first block (B); c) determining a cost surface () for each first blocks (B) and its respective search region; d) determining an averaged cost surface () for one of the at least two first blocks (B) based on the cost surfaces (); d) identifying a motion vector (v) for the one of the first blocks (B) describing a motion of a location of the first block (B) in the first image and the corresponding second block (B) in the second image. The invention also relates to a computing device (), a driver assistance system () as well as a motor vehicle ().

Method for motion estimation between two images of an environmental region of a motor vehicle, computing device, driver assistance system as well as motor vehicle

The invention relates to a method for calibrating a camera system () of a motor vehicle (). At least one camera (-) respectively sequentially generates camera images (-) from an environment () of the motor vehicle (). A computing device () generates a virtual view () of the environment () from a virtual perspective from the camera images (-) by means of a projection (P). In traveling, the computing device () passes at least once a calibration cycle for each camera (-). Based on current projection parameters () of the projection (P), therein, camera images () of the camera () are transformed. Motion vectors are determined from it. For at least one geometric characteristic of the motion vectors, a reference value is set. From a difference between the at least one geometric characteristic of the motion vectors and the respective corresponding reference value, an error value is determined. Depending on the error value, new projection parameters are determined ().

Online calibration of a motor vehicle camera system

A camera mounted on or for a vehicle has camera rotational parameters &#x3c6;, &#x3b8;, &#x3c8; and camera translational parameters x, y, zin a camera image coordinate system, and the vehicle has a vehicle coordinate system. A method for online or on-the-fly calibration of the camera involves two independent steps, namely while the vehicle is moving relative to the ground, calibrating the camera rotational parameters using a parallel geometrical calibration process, and calibrating at least some of the camera translational parameters x, yindependently of the camera rotational parameters.

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Tuam, Ireland

Pantelis Ermilios