Abstract: Example embodiments relate to calibration systems for light detection and ranging (lidar) devices. An example calibration system includes a calibration target that includes a surface having at least one characterized reflectivity. The surface is configured to receive one or more calibration signals emitted by a lidar device along one or more optical axes when the lidar device is separated from the calibration target by an adjustable distance. The calibration system also includes at least one lens that modifies the one or more calibration signals. In addition, the system includes an adjustable attenuator configured to attenuate each of the one or more calibration signals to simulate, in combination with the at least one lens, a distance that is greater than the adjustable distance. Further, the system includes a calibration controller configured to analyze data associated with detected reflections of the one or more calibration signals.

Abstract: 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.

Abstract: Example embodiments relate to temporally modulated light emission and defect detection in light detection and ranging (lidar) devices and cameras. An example embodiment includes a method. The method includes detecting, by a first detector via an optical component, a background signal corresponding to a surrounding environment. The method also includes illuminating, by a first light source, a first portion of the optical component with a first light signal. Additionally, the method includes detecting, by the first detector when one or more defects are present in a body of the first portion of the optical component or on a surface of the first portion of the optical component, the first light signal. Further, the method includes determining, by a computing device, when one or more defects are present in the body of the first portion of the optical component or on the surface of the first portion of the optical component.

Abstract: 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.

Abstract: Example embodiments relate to calibration systems for light detection and ranging (lidar) devices. An example calibration system includes a calibration target that includes a surface having at least one characterized reflectivity. The surface is configured to receive one or more calibration signals emitted by a lidar device along one or more optical axes when the lidar device is separated from the calibration target by an adjustable distance. The calibration system also includes at least one lens that modifies the one or more calibration signals. In addition, the system includes an adjustable attenuator configured to attenuate each of the one or more calibration signals to simulate, in combination with the at least one lens, a distance that is greater than the adjustable distance. Further, the system includes a calibration controller configured to analyze data associated with detected reflections of the one or more calibration signals.

Abstract: 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.

Abstract: Example embodiments relate to enhanced depth of focus cameras using variable apertures and pixel binning. An example embodiment includes a device. The device includes an image sensor. The image sensor includes an array of light-sensitive pixels and a readout circuit. The device also includes a variable aperture. Additionally, the device includes a controller that is configured to cause: the variable aperture to adjust to a first aperture size when a high-light condition is present, the variable aperture to adjust to a second aperture size when a low-light condition is present, the readout circuit to perform a first level of pixel binning when the high-light condition is present, and the readout circuit to perform a second level of pixel binning when the low-light condition is present. The second aperture size is larger than the first aperture size. The second level of pixel binning is greater than the first level of pixel binning.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Example embodiments relate to arrays of light detectors with a corresponding array of optical elements. An example embodiment includes a light detection and ranging (LIDAR) system. The LIDAR system includes an array of light detectors. The LIDAR system also includes a shared imaging optic. Further, the LIDAR system includes an array of optical elements positioned between the shared imaging optic and the array of light detectors. Each light detector in the array of light detectors is configured to detect a respective light signal from a respective region of a scene. Each respective light signal is transmitted via the shared imaging optic and modified by a respective optical element in the array of optical elements based on at least one aspect of the scene.

Abstract: Example embodiments relate to arrays of light detectors with a corresponding array of optical elements. An example embodiment includes a light detection and ranging (LIDAR) system. The LIDAR system includes an array of light detectors. The LIDAR system also includes a shared imaging optic. Further, the LIDAR system includes an array of optical elements positioned between the shared imaging optic and the array of light detectors. Each light detector in the array of light detectors is configured to detect a respective light signal from a respective region of a scene. Each respective light signal is transmitted via the shared imaging optic and modified by a respective optical element in the array of optical elements based on at least one aspect of the scene.

Abstract: Example embodiments relate to light detection and ranging (lidar) devices having a light-guide manifold. An example lidar device includes a transmit subsystem. The transmit subsystem includes a light emitter. The transmit subsystem also includes a light-guide manifold optically coupled to the light emitter. Further, the transmit subsystem includes a telecentric lens assembly optically coupled to the light-guide manifold. The lidar device also includes a receive subsystem. The receive subsystem includes the telecentric lens assembly. The receive subsystem also includes an aperture plate having an aperture defined therein. The aperture plate is positioned at a focal plane of the telecentric lens assembly. Further, the receive subsystem includes a silicon photomultiplier (SiPM) positioned to receive light traveling through the aperture.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Example embodiments relate to arrays of light detectors with a corresponding array of optical elements. An example embodiment includes a light detection and ranging (LIDAR) system. The LIDAR system includes an array of light detectors. The LIDAR system also includes a shared imaging optic. Further, the LIDAR system includes an array of optical elements positioned between the shared imaging optic and the array of light detectors. Each light detector in the array of light detectors is configured to detect a respective light signal from a respective region of a scene. Each respective light signal is transmitted via the shared imaging optic and modified by a respective optical element in the array of optical elements based on at least one aspect of the scene.