Hitachi, Ltd.

Hitachi Astemo Ltd.

Hitachi Automotive Systems, Ltd.

Alex Masuo Kaneko

Kenjiro Yamamoto

Shigenori Hayase

Akira Kuriyama

Toshiharu Sugawara

Hideyuki Kume

Taiki Iimura

Shinya Ootsuji

An object is to provide a self-position estimation device capable of highly accurately estimating self-location and attitudes on a map containing information such as lane centerlines, stop lines, and traffic rules. A representative self-position estimation device according to the present invention includes a self-position estimation portion that estimates a self-location and attitude on a high-precision map from measurement results of a sensor to measure objects around a vehicle; a low-precision section detection portion that detects a low-precision section indicating low estimation accuracy based on the self-location and attitude estimated by the self-position estimation portion; and a self-map generation portion that generates a self-generated map saving a position and type of the object on the high-precision map in the low-precision section detected by the low-precision section detection portion.

Self-position estimation device, self-driving system including the same, and self-generated map sharing device

Provided is a position estimation device capable of highly accurate position estimation. A position estimation deviceof the present invention is the position estimation devicewhich estimates a current position of a moving objectequipped with an imaging device, estimates the current position of the moving object, create a plurality of virtual positions based on the current position, creates virtual images at the plurality of virtual positions, respectively, compares the plurality of virtual images with an actual image to calculate a comparison error, calculates a weight based on at least one of information acquired by the imaging deviceand information of a current position error of the moving object, performs weighting on the comparison error using the weight, and corrects the current position based on the comparison error to be weighted.

Position estimating device

A position estimating device is provided with an image acquiring unit that acquires images around a moving body, a moving body movement amount estimating unit that estimates an amount of movement of the moving body based on the images, and a moving body position estimating unit that estimates the position of the moving body based on the estimated amount of movement. The moving body movement amount estimating unit includes a tracking unit that tracks a first feature point to a second feature point, a distance calculating unit that calculates the distance from the moving body to the second feature point, an azimuth estimating unit that estimates the azimuth of the moving body based on the distance from the moving body to the second feature point, and a movement amount estimating unit which estimates the amount of movement of the moving body based on the azimuth of the moving body.

Improvement in the accuracy of estimating the position of a mobile entity even while traveling or if there is an error in the calibration performed utilizing: a mobile entity; an imaging device provided in the mobile entity; and an information processing device for determining a first movement amount by which a detection point that is the same object has moved on the basis of a first image and a second image acquired by the imaging device and a second movement amount by which the mobile entity has moved while the first image and the second image were acquired, determining the accuracy of recognizing the detection point acquired by the imaging device on the basis of the first movement amount and the second movement amount, and estimating the position of the mobile entity on the basis of the accuracy of recognition and position information that pertains to the detection point.

Mobile entity position estimation device and position estimation method

A distance to an object is estimated with a monocular camera that estimates a distance from a moving object to feature points on an image from an imaging device mounted on the moving object. The distance estimator sets one or more feature points on the image acquired from the imaging device at a first timing and detects the feature point on the image acquired from the imaging device at a second timing. The distance estimator also determines the movement amount of the feature point on the image between the first timing and the second timing and determines the movement amount of the moving object between the first and second timings. The distance estimator then estimates the distance from the moving object to the feature point based on the movement amount on the image and the movement amount of the moving object between the first and second timings.

Distance estimation apparatus and method

An object of the invention is to provide a moving object capable of being calculated a movement amount with a low processing load and high accuracy even when a three-dimensional object which is stationary or moves appears in a camera image. The invention is directed to a moving object which includes an imaging device which captures an image of a road surface and an image processing unit which calculates a movement amount of the moving object based on an image captured by the imaging device, wherein the image processing unit extracts a plurality of first feature points from a first image captured at a first timing, extracts a plurality of second feature points from a second image captured at a second timing after the first timing, performs tracking on each of the plurality of first feature points to each of the plurality of second feature points, calculates a movement amount and a speed of each of the plurality of second feature points, and calculates a movement amount of the moving object based on a feature point having a speed within a predetermined range among the plurality of second feature points.

Moving object

The reliability of detection of the traveling lane can be improved even in a case where an image capturing environment changes. The lane detection device includes a first image capturing device which is fixed to a moving body and periodically captures a first image under a predetermined first image capturing condition, a second image capturing device which is fixed to the moving body and periodically captures a second image under a second image capturing condition different from the predetermined first image capturing condition, and an operation device, regarding a traveling lane on which the moving body travels, which detects a first boundary line of the traveling lane on the first image, detects a second boundary line of the traveling lane on the second image, and estimates the position of the first boundary line on the first image based on a position of the second boundary line on the second image under a predetermined execution condition.

Lane detection device and lane detection method

Landmark recognition can handle various landmarks with a low processing load and high reliability. Provided are a database, a segmentation processor, a pattern extraction processor, and a database comparison processor. The database stores landmark extraction information associated with landmark determination condition information, the landmark extraction information identifying a region including a landmark of an image, and allowing right-opposite positioning of the landmark, the landmark determination condition information used for determining landmark pattern information as the landmark. The segmentation processor sets a plurality of segmentation lines with respect to the region including the landmark of the image in a direction parallel to the landmark from a direction right opposite to the landmark, and determines a distance between intersections of the segmentation lines and a boundary of the landmark. The pattern extraction processor obtains the landmark pattern information that represents a characteristic of the pattern of the landmark as numerical value information in response to the distance between the intersections determined for each of the plurality of segmentation lines. The database comparison processor compares the landmark pattern information extracted in the pattern extraction processor with the landmark determination condition information registered in the database to identify the kind of the landmark.

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Tokyo, Japan

Alex Masuo Kaneko