Abstract: Time of Flight (ToF) depth image processing methods are disclosed for resolving corruption of ToF depth images. In ToF depth imaging, the light can travel paths of different lengths before returning to the pixel. Thus, the light hitting the pixel can have travelled different distances, and the distance obtained from an estimation procedure assuming a single distance may be spurious. Systems and methods are disclosed for including a time-of-flight imager and processor which resolves the multiple paths, and outputs the multiple depths at each pixel.

Abstract: An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Abstract: A sensor system that obtains and processes time-of-flight data (TOF) obtained in an arbitrary orientation is provided. A TOF sensor obtains distance data describing various surfaces. A processor identifies a horizontal Z-plane in the environment, and transforms the data to align with the Z-plane. In some embodiments, the environment includes a box, and the processor identifies a bottom and a top of the box in the transformed data. The processor can further determine dimensions of the box, e.g., the height between the top and bottom of the box, and the length and width of the box top.

Abstract: Systems and apparatus for phase calibration in time-of-flight cameras. In particular, systems and methods are presented for a miniaturized cover design that at least partially encloses the time-of-flight (ToF) module. The geometry of the miniaturized design causes the signals reflected from the calibration device back to the ToF imager to have essentially the same time-of-flight. The design of the calibration device prevents the modulated emissions from leaking out to the environment.

Abstract: A depth estimation system can use a guided filter to enhance depth estimation using brightness image. Light is projected onto an object. The object reflects at least a portion of the projected light. The reflected light is at least partially captured by an image sensor. The depth estimation system may generate a depth image based on a phase shift between the captured light and the projected light and generate a brightness image based on brightness of the captured light. The depth estimation system may use the guided filter to identify a pixel that represents at least a portion of a boundary of the object. The guided filter can determine a depth value of the pixel based on the value of the corresponding pixel in the brightness image. The depth estimation system can assign the depth value to the pixel and generates an enhanced depth image.

Abstract: A sensor system that obtains and processes time-of-flight data (TOF) is provided. A TOF sensor obtains raw data describing various surfaces. A processor applies an averaging filter to the raw data to smooth the raw data for increasing signal-to-noise ratio (SNR) of flat surfaces represented in the raw data, performs a depth compute process on the raw data, as filtered, to generate distance data, generates a point cloud based on the distance data, and identifies the Z-planes in the point cloud.

Abstract: An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Abstract: A method collects radar signals reflected from particles distributed by an emission device. A three-dimensional range-angle-velocity cube is formed from the radar signals. The three-dimensional range-angle-velocity cube includes individual bins with radar intensity values characterizing angle and range for a specific velocity. The three-dimensional range-angle-velocity cube is analyzed to identify a ground plane and radar signals reflected from particles immediately proximate to the ground plane. Total particle deposition distribution is predicted.

Abstract: Systems and methods are disclosed for phase unwrapping for time-of-flight imaging. A method is provided for phase unwrapping that includes measuring a plurality of wrapped depths at a respective plurality of frequencies, wherein each of the plurality of wrapped depths corresponds to a respective phase, generating a plurality of unwrapped phases based on a probability distribution function, by unwrapping each of the plurality of wrapped depths, and identifying a Voronoi cell.

Abstract: System and apparatus for pulse oximetry. Systems, methods, and devices are presented for placement of the hardware on a body part where detracting signals are strong. In one example, detracting signals include signals from respiration. In various examples, the body part the oximeter is placed on is a wrist, chest, or arm. A computationally efficient pipeline is presented that allows for reliable and robust SpO2 estimation.

Abstract: An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Abstract: Time of Flight (ToF) depth image processing methods. Depth edge preserving filters are disclosed with superior performance to standard edge preserving filters applied to depth maps. In particular, depth variance is estimated and used to filter while preserving depth edges. In doing so, filter strength is calculated which can be used as an edge detector. A confidence map is generated with low confidence at pixels straddling a depth edge, and which reflects the reliability of the depth measurement at each pixel.

Abstract: Depth imagers can implement time-of-flight operations to measure depth or distance of objects. A depth imager can emit light onto a scene and sense light reflected back from the objects in the scene using an array of sensors. Timing of the reflected light hitting the array of sensors gives information about the depth or distance of objects in the scene. In some cases, corrupting light that is outside of a field of view of a pixel in the array of sensors can hit the pixel due to internal scattering or internal reflections occurring in the depth imager. The corrupting light can corrupt the depth or distance measurement. To address this problem, an improved depth imager can isolate and measure the corrupting light due to internal scattering or internal reflections occurring in the depth imager, and systematically remove the measured corrupting light from the depth or distance measurement.

Abstract: Aspects of the embodiments are directed to passive depth determination. Initially, a high power depth map of a scene can be created. An object in the scene can be identified, such as a rigid body or other object or portion of an object. A series of lower power or RGB images can be captured. The object can be located in one or more of the lower power or RGB images. A change in the position of an object, represented by a set of pixels, can be determined. From the change in position of the object, a new depth of the object can be extrapolated. The extrapolated depth of the object can be used to update the high power depth map.

Abstract: Time of Flight (ToF) depth image processing methods are disclosed for resolving corruption of ToF depth images. In ToF depth imaging, the light can travel paths of different lengths before returning to the pixel. Thus, the light hitting the pixel can have travelled different distances, and the distance obtained from an estimation procedure assuming a single distance may be spurious. Systems and methods are disclosed for including a time-of-flight imager and processor which resolves the multiple paths, and outputs the multiple depths at each pixel.

Abstract: An image processing system having on-the-fly calibration uses the placement of the imaging sensor and the light source for calibration. The placement of the imaging sensor and light source with respect to each other affect the amount of signal received by a pixel as a function of distance to a selected object. For example, an obstruction can block the light emitter, and as the obstruction is positioned an increasing distance away from the light emitter, the signal level increases as light rays leave the light emitters, bounce off the obstruction and are received by the imaging sensor. The system includes a light source configured to emit light, and an image sensor to collect incoming signals including reflected light, and a processor to determine a distance measurement at each of the pixels and calibrate the system.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables where the cost functions have a relationship with the variables and receiving a landmark indicating a point that an agent is to visit while moving, a cost being associated with ignoring the landmark. The method includes generating a first message for the cost functions for the corresponding variable based upon the relationship and a second message for each of the variables for the corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes repeating steps (c), (d), and (e) until a consensus is formed between the first and second messages until the optimization solution is determined based upon the consensus.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables in which each of the cost functions has a predetermined relationship with select ones of the variables. The variables comprise a sub-solution of a spline indicative of a curved path along an estimated trajectory. The method includes generating a first message for each of the cost functions for each corresponding variable based upon the relationship and a second message for each of the variables for each corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes forming a consensus between the first and second messages until the optimization solution is determined.

Abstract: Depth imagers can implement time-of-flight operations to measure depth or distance of objects. A depth imager can emit light onto a scene and sense light reflected back from the objects in the scene using an array of sensors. Timing of the reflected light hitting the array of sensors gives information about the depth or distance of objects in the scene. In some cases, corrupting light that is outside of a field of view of a pixel in the array of sensors can hit the pixel due to internal scattering or internal reflections occurring in the depth imager. The corrupting light can corrupt the depth or distance measurement. To address this problem, an improved depth imager can isolate and measure the corrupting light due to internal scattering or internal reflections occurring in the depth imager, and systematically remove the measured corrupting light from the depth or distance measurement.

Abstract: Aspects of the embodiments are directed to methods and imaging systems. The imaging systems can be configured to sense, by an light sensor of the imaging system, light received during a time period, process the light received by the light sensor, identify an available measurement period for the imaging system within the time period based on the processed light, and transmit and receive light during a corresponding measurement period in one or more subsequent time periods.