Abstract: This disclosure describes a method of controlling an unmanned aerial vehicle (UAV). The steps of controlling include acquiring images with an image capture device of an unmanned aerial vehicle (UAV). The steps include analyzing the images to determine navigation information of the UAV with a vision-based navigation system. The steps include tracking a position of the UAV with the vision-based navigation system. The steps include controlling rotors of the UAV to prevent deviations in movement from a desired flight path or position of the UAV. The steps include limiting travel or flight of the UAV to a physical region determined by the desired flight path.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. A disambiguated depth of a surface may be determined from the first image data and the second image data. If the disambiguated depth is greater than a nominal maximum depth of the sensor in the first configuration, an intensity of the surface may be determined from the first image data. If the intensity meets or exceeds a threshold intensity, the surface may be determined to be beyond the nominal maximum depth. If the intensity is less than the threshold intensity, an actual depth of the surface may be determined form the second image data as a distance less than the nominal maximum depth.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include saturated pixels, e.g., due to over-exposure, sensing highly-reflective objects, and/or excessive ambient light. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on characteristics of the saturated pixels, as well as information about non-saturated pixels neighboring the saturated pixels. For example, the neighboring pixels may provide information about whether saturation is due to ambient light, e.g., sunlight, or due to emitted light from the sensor.

Abstract: A machine-learned (ML) model for detecting that depth data (e.g., lidar data, radar data) comprises a false positive attributable to particulate matter, such as dust, steam, smoke, rain, etc. The ML model may be trained based at least in part on simulated depth data generated by a fluid dynamics model and/or by collecting depth data during operation of a device (e.g., an autonomous vehicle. In some examples, an autonomous vehicle may identify depth data that may be associated with particulate matter based at least in part on an outlier region in a thermal image. For example, the outlier region may be associated with steam.

Abstract: The present teachings provide a non-transitory computer-readable storage medium, including a processor-executable routines that, when executed by a processor, facilitate a performance of operations. The operations include detecting a condition using one or more sensors of a vehicle, wherein the vehicle includes motors. The operations include signaling the condition by causing one of the motors of the vehicle to produce an audible tone. The operations include defining an operating margin for the one of the motors to produce the audible tone. The operations include changing an amount of operating current supplied to the one of the motors based on a maximum amount of current that can be supplied to the one of the motors based on the operating margin.

Abstract: This disclosure describes a method of controlling an unmanned aerial vehicle (UAV). The steps of controlling include acquiring images with an image capture device of an unmanned aerial vehicle (UAV). The steps include analyzing the images to determine navigation information of the UAV with a vision-based navigation system. The steps include tracking a position of the UAV with the vision-based navigation system. The steps include controlling rotors of the UAV to prevent deviations in movement from a desired flight path or position of the UAV. The steps include limiting travel or flight of the UAV to a physical region determined by the desired flight path.

Abstract: A condition of an unmanned aerial vehicle (UAV) is detected using one or more sensors of the UAV and signaled according to an alert definition associated with the condition. For example, an alert definition can indicate to signal the condition by using a motor of the UAV to produce an audible tone. A tonal signal having a frequency within an audible spectrum can be generated according to the alert definition. The tonal signal and a drive signal used for supplying current to the motor can be combined to produce a combined signal. The combined signal can then be transmitted to the motor to cause the motor to produce the audible tone. In some cases, an amplitude of the tonal signal can be modulated, such as where the amplitude of the combined signal exceeds a threshold associated with an operating margin of the UAV.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over- or under-exposure. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on a number of unreliable pixels in measured data and/or based on intensity values of the measured data. For example, unreliable pixels can be determined using phase frame information captured at a receiver of the sensor.

Abstract: This disclosure describes systems and methods for a multipoint cable cam (MPCC) of an aerial vehicle. A method includes operations of receiving user input associated with a predetermined path and correlating the received user input with stored global positioning satellite (GPS) data to generate one or more virtual waypoints along the predetermined path. The method includes processing the one or more virtual waypoints to generate a spline-based flight path. The method may include storing the spline-based flight path and transmitting the spline-based flight path to the aerial vehicle.

Abstract: A LIDAR system that identifies, from a channel output, a false positive return and/or suppressing a corresponding false positive detection caused, in some cases, a strong reflection by a highly reflective surface that caused light to leak from a first channel to a second channel. The LIDAR system described herein may identify, as a false return, a return detected in the second channel that has an intensity that is much less than a return in the first channel and indicates a distance that is the same or very close to a distance indicated the return in the first channel. Based at least in part on identifying a return as a false return, the LIDAR system may suppress a false detection associated with the false return by modifying a detection threshold.

Abstract: Techniques for facilitating a robust clock synchronization across a computer network that presumes network jitter exists are discussed herein. A first device and a second device transceive a plurality of sets of time-synchronization messages to synchronize a synchronization clock of the second device to a first clock of the first device. The second device calculates a smoothing of time delay data of a plurality of sets. The time delay data is associated with a transmission duration of time-synchronization messages of the sets of the plurality. The second device sets a synchronization clock based on a time at the first device and the smoothed time delay data.

Abstract: Particulate matter, such as dust, steam, smoke, rain, etc. may cause one or more sensor types to generate false positive detections. In particular, various depth measurements may be impeded by particulate matter. Identifying a false return and/or removing a false detection based at least in part on a sensor output may comprise determining a similarity of a portion of a return signal to an emitted light pulse or an expected return signal, determining a variance of the signal portion over time, determining a difference between a power spectrum of the return relative to an expected power spectrum, and/or determining that a duration associated with the signal portion meets or exceeds a threshold duration.

Abstract: Techniques for facilitating a robust clock synchronization across a computer network that presumes network jitter exists are discussed herein. A first device and a second device transceive a plurality of sets of time-synchronization messages to synchronize a synchronization clock of the second device to a first clock of the first device. The second device calculates a smoothing of time delay data of a plurality of sets. The time delay data is associated with a transmission duration of time-synchronization messages of the sets of the plurality. The second device sets a synchronization clock based on a time at the first device and the smoothed time delay data.

Abstract: Techniques associated with generating or tuning parameters associated with long wave infrared sensor data to improve object detection associated with the captured images are discussed herein. The system may determine a region of interest associated with the sensor data and adjust or tune the parameters to improve detection(s) within the region of interest. Additionally, the system may adjust the parameters based on map data and/or environmental conditions, such as weather and temperature.

Abstract: Techniques associated with generating or tuning parameters associated with long wave infrared sensor data to improve object detection associated with the captured images are discussed herein. The system may determine a region of interest associated with the sensor data and adjust or tune the parameters to improve detection(s) within the region of interest. Additionally, the system may adjust the parameters based on map data and/or environmental conditions, such as weather and temperature.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. An estimated depth of an object may be determined from the first image data, and an actual depth of the object may be determined from the second image data, based on the estimated depth. In examples, the first and second configurations have different modulation frequencies such that a nominal maximum depth in the first configuration is greater than the nominal maximum depth in the second configuration.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. A disambiguated depth of a surface may be determined from the first image data and the second image data. If the disambiguated depth is greater than a nominal maximum depth of the sensor in the first configuration, an intensity of the surface may be determined from the first image data. If the intensity meets or exceeds a threshold intensity, the surface may be determined to be beyond the nominal maximum depth. If the intensity is less than the threshold intensity, an actual depth of the surface may be determined form the second image data as a distance less than the nominal maximum depth.

Abstract: This disclosure describes systems and methods for a multipoint cable cam (MPCC) of an aerial vehicle. A method includes operations of receiving user input associated with a predetermined path and correlating the received user input with stored global positioning satellite (GPS) data to generate one or more virtual waypoints along the predetermined path. The method includes processing the one or more virtual waypoints to generate a spline-based flight path. The method may include storing the spline-based flight path and transmitting the spline-based flight path to the aerial vehicle.