Abstract: The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Abstract: The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: The present disclosure relates to systems and methods that facilitate light detection and ranging operations. An example transmit block includes at least one substrate with a plurality of angled facets. The plurality of angled facets provides a corresponding plurality of elevation angles. A set of angle differences between adjacent elevation angles includes at least two different angle difference values. A plurality of light-emitter devices is configured to emit light into an environment along the plurality of elevation angles toward respective target locations so as to provide a desired resolution and/or a respective elevation angle. The present disclosure also relates to adjusting shot power and a shot schedule based on the desired resolution and/or a respective elevation angle.

Abstract: A mobile dimensioning device, i.e. a mobile dimensioner, is described that uses a dynamic accuracy while still being compatible with the NIST standard. Even if the accuracy division is dynamic and not predetermined, a mobile dimensioning device of the present invention reports the actual dimensioning prior to measurement capture and can therefore be certified and used in commercial transactions.

Abstract: The present disclosure relates to systems and methods that facilitate light detection and ranging operations. An example transmit block includes at least one substrate with a plurality of angled facets. The plurality of angled facets provides a corresponding plurality of elevation angles. A set of angle differences between adjacent elevation angles includes at least two different angle difference values. A plurality of light-emitter devices is configured to emit light into an environment along the plurality of elevation angles toward respective target locations so as to provide a desired resolution and/or a respective elevation angle. The present disclosure also relates to adjusting shot power and a shot schedule based on the desired resolution and/or a respective elevation angle.

Abstract: A system and process to determine whether a digital image has been manipulated involves determining expected locations of non-standard pixels in the digital image, and determining a feature for evaluating the non-standard pixels. The feature in pixels of the digital image that are located at the expected locations of non-standard pixels is then measured, and a statistical measure of the feature of pixels in the digital image that are located at the expected locations of non-standard pixels is evaluated. The digital image is assessed to determine a probability that the digital image includes a manipulated portion, based on the statistical measure. The system and process can also determine a make and model of an image sensing device via an examination of the non-standard pixels.

Abstract: Various embodiments described herein relate to techniques for computing dimensions of an object. In this regard, a dimensioning system converts points cloud data associated with an object into a density image for a scene associated with the object. The dimensioning system also segments the density image to determine a void region in the density image that corresponds to the object. Furthermore, the dimensioning system determines, based on the void region for the density image, dimension data indicative of one or more dimensions of the object.

Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.

Abstract: Various embodiments described herein relate to techniques for computing dimensions of an object. In this regard, a dimensioning system converts points cloud data associated with an object into a density image for a scene associated with the object. The dimensioning system also segments the density image to determine a void region in the density image that corresponds to the object. Furthermore, the dimensioning system determines, based on the void region for the density image, dimension data indicative of one or more dimensions of the object.

Abstract: The present disclosure relates to systems and methods for occlusion detection. One example method involves a light detection and ranging (LIDAR) device scanning at least a portion of an external structure within a field-of-view (FOV) of the LIDAR device. The LIDAR device is physically coupled to the external structure. The scanning comprises transmitting light pulses toward the external structure through an optical window, and receiving reflected light pulses through the optical window. The reflected light pulses comprise reflections of the transmitted light pulses returning back to the LIDAR device from the external structure. The method also involves detecting presence of an occlusion that at least partially occludes the LIDAR device from scanning the FOV based on at least the scan of the at least portion of the external structure.

Abstract: Various embodiments described herein relate to techniques for computing dimensions of an object using multiple range images. In this aspect, the multiple range images are captured from selective locations and satisfy a pre-defined criterion. In accordance with various embodiments, at least a pair of 3D points are identified from the multiple range images, which correspond to at least one geometric feature on a surface of the object. In this regard, a correspondence score is estimated for the identified at least one pair of 3D points. The correspondence score is then utilized for registering the at least one pair of 3D points. Based in part on the registration of the 3D points and 3D point clouds retrieved from the captured images, the dimensions of the object are computed.

Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.

Abstract: A terminal for measuring at least one dimension of an object includes a range camera, a visible camera, and a display that are fixed in position and orientation relative to each other. The range camera is configured to produce a range image of an area in which the object is located. The visible camera is configured to produce a visible image of an area in which the object is located. The display is configured to present information associated with the range camera's field of view and the visible camera's field of view.

Abstract: Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: The present disclosure relates to systems and methods that facilitate light detection and ranging operations. An example transmit block includes at least one substrate with a plurality of angled facets. The plurality of angled facets provides a corresponding plurality of elevation angles. A set of angle differences between adjacent elevation angles includes at least two different angle difference values. A plurality of light-emitter devices is configured to emit light into an environment along the plurality of elevation angles toward respective target locations so as to provide a desired resolution and/or a respective elevation angle. The present disclosure also relates to adjusting shot power and a shot schedule based on the desired resolution and/or a respective elevation angle.

Abstract: Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Abstract: A system and process to determine whether a digital image has been manipulated involves determining expected locations of non-standard pixels in the digital image, and determining a feature for evaluating the non-standard pixels. The feature in pixels of the digital image that are located at the expected locations of non-standard pixels is then measured, and a statistical measure of the feature of pixels in the digital image that are located at the expected locations of non-standard pixels is evaluated. The digital image is assessed to determine a probability that the digital image includes a manipulated portion, based on the statistical measure. The system and process can also determine a make and model of an image sensing device via an examination of the non-standard pixels.