Waymo LLC

Gil Shotan

Luke Wachter

Colin Andrew Braley

Christian Lauterbach

Xiaoxiang Hu

Joshua Ruizhi Wang

Ming Zou

Blaise Gassend

Volodymyr V Ivanchenko

Mark Alexander Shand

Mingcheng Chen

Volker Grabe

Nathaniel Quillin

One example method involves obtaining a plurality of scans of a field-of-view (FOV) of a light detection and ranging (LIDAR) device disposed inside a housing. Obtaining each scan of the plurality of scans comprises: transmitting, through a plurality of sections of the housing, a plurality of light pulses emitted from the LIDAR device in different directions toward the housing; and detecting a plurality of returning light pulses comprising reflected portions of the transmitted plurality of light pulses that are reflected back toward the LIDAR device. The method also involves detecting an obstruction that at least partially occludes the LIDAR device from scanning the FOV through the housing based on the plurality of scans.

Methods and systems for detecting obstructions on a sensor housing

Aspects and implementations of the present disclosure relate to augmenting point cloud data with artificial return points. An example method includes: receiving point cloud data comprising a plurality of return points, each return point being representative of a reflecting region that reflects a beam emitted by a sensing system, and generating a plurality of artificial return points based on presence or absence of return points along radial paths of beams emitted from the sensing system.

Augmenting point cloud data with artificial return points

Example embodiments relate to methods for localizing light detection and ranging (lidar) calibration targets. An example method includes generating a point cloud of a region based on data from a light detection and ranging (lidar) device. The point cloud may include points representing at least a portion of a calibration target. The method also includes determining a presumed location of the calibration target. Further, the method includes identifying, within the point cloud, a location of a first edge of the calibration target. In addition, the method includes performing a comparison between the identified location of the first edge of the calibration target and a hypothetical location of the first edge of the calibration target within the point cloud if the calibration target were positioned at the presumed location. Still further, the method includes revising the presumed location of the calibration target based on at least the comparison.

Methods for localizing light detection and ranging (lidar) calibration targets

One example method involves obtaining a plurality of images using a camera located at a given position relative to a light detection and ranging device (LIDAR). A first image of the plurality may be indicative of a view, via one or more optical elements of the LIDAR, of a receiver of the LIDAR. The method also involves determining simulated detector positions for intercepting reflections of simulated light beams associated with a plurality of light sources in a transmitter of the LIDAR. The method also involves determining one or more alignment offsets between the transmitter and the receiver based on at least the simulated detector positions and the plurality of images.

LIDAR optics alignment system

Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.

Computation of the angle of incidence of laser beam and its application on reflectivity estimation

Example embodiments relate to multistage camera calibration. An example embodiment includes a method. The method includes positioning a calibration target relative to a camera. The method also includes capturing a focused image of the calibration target. Further, the method includes determining one or more approximate locations of calibration points of the calibration target within the focused image of the calibration target. In addition, the method includes adjusting the focal length of the camera. Still further, the method includes capturing an unfocused image of the calibration target. Additionally, the method includes determining, based on the one or more approximate locations of calibration points of the calibration target within the focused image of the calibration target, one or more refined locations of calibration points of the calibration target within the unfocused image of the calibration target. Yet further, the method includes correcting for one or more optical aberrations within the camera.

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San Francisco, CA, United States of America

Gil Shotan