- Mountain View CA, US Giulia Guidi - Mountain View CA, US Ralph Shepard - Menlo Park CA, US Vlad Cardei - Redwood City CA, US Lucian Ion - Santa Clara CA, US
International Classification:
G01D 5/353 G01S 13/89 H04N 5/33
Abstract:
A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.
- Mountain View CA, US John Henrie - Los Altos CA, US Chandra Kakani - Fremont CA, US Ralph Shepard - Menlo Park CA, US Drew Ulrich - San Francisco CA, US
International Classification:
G02B 7/02
Abstract:
The technology relates to lens assemblies for sensor units that provide a low but consistent preload force over the entire operational temperature range of the device. Consistent preloading helps to avoid cracking and plastic deformation. In particular, a compliant structure of a polymeric material is able to expand and contract across temperature extremes. In addition, the polymeric material is arranged in conjunction with a retainer ring to form a discontinuous seal with the lens. This provides in a leak path that is able to reduce condensation or contaminants. As a result, moisture within the sensor unit is permitted to escape, reducing or eliminating impairments on the lens or other parts of the sensor unit that could otherwise impair device operation.
Example embodiments relate to LIDAR systems with multi-faceted mirrors. An example embodiment includes a LIDAR system. The system includes a multi-faceted mirror that includes a plurality of reflective facets, which rotates about a first rotational axis. The system also includes a light emitter configured to emit a light signal toward one or more regions of a scene. Further, the system includes a light detector configured to detect a reflected light signal. In addition, the system includes an optical window positioned between the multi-faceted mirror and the one or more regions of the scene such that light reflected from one or more of the reflective facets is transmitted through the optical window. The optical window is positioned such that the optical window is non-perpendicular to the direction toward which the light emitted along the optical axis is directed for all angles of the multi-faceted mirror.
Example embodiments relate to beam homogenization for occlusion avoidance. One embodiment includes a light detection and ranging (LIDAR) device. The LIDAR device includes a transmitter and a receiver. The transmitter includes a light emitter. The light emitter emits light that diverges along a fast-axis and a slow-axis. The transmitter also includes a fast-axis collimation (FAC) lens optically coupled to the light emitter. The FAC lens is configured to receive light emitted by the light emitter and reduce a divergence of the received light along the fast-axis of the light emitter to provide reduced-divergence light. The transmitter further includes a transmit lens optically coupled to the FAC lens. The transmit lens is configured to receive the reduced-divergence light from the FAC lens and provide transmit light. The FAC lens is positioned relative to the light emitter such that the reduced-divergence light is expanded at the transmit lens.
- Mountain View CA, US YooJung Ahn - Mountain View CA, US Jared Gross - Belmont CA, US Joshua Newby - San Francisco CA, US Jerry Chen - San Francisco CA, US Ralph Shepard - Menlo Park CA, US Adam Brown - Mountain View CA, US
The technology employs a contrasting color scheme on different surfaces for sensor housing assemblies mounted on exterior parts of a vehicle that is configured to operate in an autonomous driving mode. Lighter and darker colors may be chosen on different surfaces according to a thermal budget for a given sensor housing assembly, due to the different types of sensors arranged along particular surfaces, or to provide color contrast for different regions of the assembly. For instance, differing colors such as black/white or blue/white, and different finishes such as matte or glossy, may be selected to enhance certain attributes or to minimize issues associated with a sensor housing assembly.
Sensor Region Of Interest Selection Based On Multisensor Data
- Mountain View CA, US Giulia Guidi - Mountain View CA, US Ralph Shepard - Menlo Park CA, US Vlad Cardei - Redwood City CA, US Lucian Ion - Santa Clara CA, US
International Classification:
G01D 5/353 H04N 5/33 G01S 13/89
Abstract:
A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.
The present disclosure relates to systems and methods that utilize machine learning techniques to improve object classification in thermal imaging systems. In an example embodiment, a method is provided. The method includes receiving, at a computing device, one or more infrared images of an environment. The method additionally includes, applying, using the computing device, a trained machine learning system on the one or more infrared images to determine an identified object type in the environment by at least: determining one or more prior thermal maps associated with the environment; using the one or more prior thermal maps and the one or more infrared images, determining a current thermal map associated with the environment; and determining the identified object type based on the current thermal map. The method also includes providing the identified object type using the computing device.
- Mountain View CA, US Pierre-Yves Droz - Mountain View CA, US Ralph Shepard - Mountain View CA, US
International Classification:
G01S 7/484 G01S 17/89 G01S 17/10
Abstract:
The present disclosure relates to systems, methods, and vehicles that could include a rotatable base configured to rotate about a first axis and a refractive optical window coupled to the rotatable base. The refractive optical window includes a flat window portion and a prism window portion or a curved refractive optical window. The LIDAR system could additionally include a mirror assembly coupled to the rotatable base. The mirror assembly includes a plurality of reflective surfaces. The mirror assembly is configured to rotate about a second axis. The second axis is substantially perpendicular to the first axis. The LIDAR system also includes a light-emitter device coupled to the rotatable base. The light-emitter device is configured to emit light pulses that interact with the mirror assembly and the refractive optical window such that the light pulses are directed into a first field of view within an environment of the LIDAR system.