A method and an apparatus for calibrating a parameter of a laser radar are provided. The cost function used to determine the predicted value of the first parameter is determined based on the three-dimensional coordinates of the sampling points and the fitting function for the sampling points. The fitting function for the plurality of sampling points uses the first parameter as an independent variable.

Techniques are described for time-of-fly sensors with hybrid refractive gradient-index optics. Some embodiments are for integration into portable electronic devices with cameras, such as smart phones. For example, a time-of-fly (TOF) imaging subsystem can receive optical information along an optical path at an imaging plane. A hybrid lens can be coupled with the TOF imaging subsystem and disposed in the optical path so that the imaging plane is substantially at a focal plane of the hybrid lens. The hybrid lens can include a less-than-quarter-pitch gradient index (GRIN) lens portion, and a refractive lens portion with a convex optical interface. The portions of the hybrid lens, together, produce a combined focal length that defines the focal plane. The hybrid lens is designed so that the combined focal length is less than a quarter-pitch focal length of the GRIN lens portion and has less spherical aberration than either lens portion.

Devices, methods, and computer program products for measuring the speed of an object. A speed measuring device includes a rangefinder module configured to measure distances from the device to a target object. Activating the device causes the device to measure a first distance from the device to the object along a first line-of-sight, and a second distance from the device to the object along a second line-of-sight. The device determines an angular displacement between the first line-of-sight and the second line-of-sight, and one or more of an elapsed time between measuring the first distance and measuring the second distance and a radial velocity of the object. The device then determines the absolute speed of the object based on the first distance, the second distance, the angular displacement, and one or more of the elapsed time and radial velocity.

A method for transforming data from the time domain to the frequency domain. The method including receiving time domain input data, the time domain input data being sparse and binary-valued, obtaining at least one time vector corresponding to times of non-zero entries in the time domain input data, obtaining a frequency vector corresponding to frequencies of interest, determining at least one matrix corresponding to the at least one time vector and the frequency vector, performing a Sparse Partial Fourier Transform (SPFT) computation using the at least one matrix, and providing frequency domain output data corresponding to the time domain input data.

Simulating data received by a detection and ranging sensor including determining a first set of sample points on a surface of a simulated detector, the first set of sample points including a first sample point at a first location on the detector and a second sample point at a second location on the detector; generating a first ray associated with the first sample point, the first ray representing electromagnetic radiation reflected at a first point of intersection within a scene and incident on the first sample point on the surface of the detector; generating a second ray associated with the second sample point; the second ray representing electromagnetic radiation reflected at a second point of intersection within the scene and incident on the second sample point on the surface of the detector; and generating a ray-based data representation based on the first ray and the second ray.

Disclosed herein is method and system for determining correctness of Lidar sensor data used for localizing autonomous vehicle. The system identifies one or more Region of Interests (ROIs) in Field of View (FOV) of Lidar sensors of autonomous vehicle along a navigation path. Each ROI includes one or more objects. Further, for each ROI, system obtains Lidar sensor data comprising one or more reflection points corresponding to the one or more objects. The system forms one or more clusters in each ROI. The system identifies a distance value between, one or more clusters projected on 2D map of environment and corresponding navigation map obstacle points, for each ROI. The system compares distance value between one or more clusters and obstacle points based on which correctness of Lidar sensor data is determined. In this manner, present disclosure provides a mechanism to detect correctness of Lidar sensor data for navigation in real-time.

A method generates synthetic sensor data corresponding to a LiDAR sensor of a vehicle, the synthetic sensor data including superimposed distance and intensity information. The method includes: providing a hierarchical variational autoencoder; conditioning a first feature vector and a second feature vector with a second data set, the second data set including distance and intensity information; combining the conditioned first feature vector and the conditioned second feature vector into a resulting third feature vector; and decoding the resulting third feature vector to generate a third data set of synthetic sensor data, the third data set including superimposed distance and intensity information.

A computer-implemented method as well as a system for determining intensity values of pixels of distance data of the pixels generated by a simulation of a 3D scene, including an assignment of a first confidence value to each of the first initial values of the pixels and/or a second confidence value to each of the second intensity values of the pixels, and including a calculation of third, in particular corrected, intensity values of the pixels, using the confidence values assigned to each of the first intensity values and/or second intensity values. The invention also relates to a computer-implemented method for providing a trained machine learning algorithm as well as to a computer program.

A computer-implemented method is provided for simulating sensor data acquisition. The method may include receiving simulated sensor data corresponding with a synthetic object in a simulated three-dimensional (3D) environment. The method may also include identifying an object type corresponding with the synthetic object based on a location of the synthetic object in the simulated 3D environment. The method may further include associating an intensity value with the simulated sensor data based on the object type for the synthetic object.

Multiple LIDAR output signals are generated and are concurrently directed to the same sample region in a field of view. The LIDAR output signals have one or more optical diversities selected from a group consisting of wavelength diversity, polarization diversity, and diversity of an angle of incidence of the LIDAR output signal relative to the sample region.