Systems and methods are disclosed to identify a presence of a volumetric medium in an environment associated with a LIDAR system. In some implementations, the LIDAR system may emit a light pulse into the environment, receive a return light pulse corresponding to reflection of the emitted light pulse by a surface in the environment, and determine a pulse width of the received light pulse. The LIDAR system may compare the determined pulse width with a reference pulse width, and determine an amount of pulse elongation of the received light pulse. The LIDAR system may classify the surface as either an object to be avoided by a vehicle or as air particulates associated with the volumetric medium based, at least in part, on the determined amount of pulse elongation.

A light detection and ranging (LIDAR) sensor assembly can comprise an optics assembly that includes a LIDAR sensor and a set of dovetail joint inserts. The LIDAR sensor assembly can further include a frame comprising a set of dovetail joint septums coupled to the set of dovetail joint inserts of the optics assembly.

A light detection and ranging (LIDAR) sensor assembly can comprise an optics assembly that includes a LIDAR sensor and a set of dovetail joint inserts. The LIDAR sensor assembly can further include a frame comprising a set of dovetail joint septums coupled to the set of dovetail joint inserts of the optics assembly.

The present disclosure relates to systems, vehicles, and methods for adjusting a pointing direction and/or a scanning region of a lidar. An example method includes determining a plurality of points of interest within an environment of a vehicle. The method also includes assigning, to each point of interest of the plurality of points of interest, a respective priority score. The method additionally includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. For each sector of the plurality of sectors, the method includes adjusting a scanning region of a lidar unit based on the respective sector and causing the lidar unit to scan the respective sector.

The present disclosure relates to systems, vehicles, and methods for adjusting a pointing direction and/or a scanning region of a lidar. An example method includes determining a plurality of points of interest within an environment of a vehicle. The method also includes assigning, to each point of interest of the plurality of points of interest, a respective priority score. The method additionally includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. For each sector of the plurality of sectors, the method includes adjusting a scanning region of a lidar unit based on the respective sector and causing the lidar unit to scan the respective sector.

Robot localization or mapping can be provided without requiring the expense or complexity of an “at-a-distance” sensor, such as a camera, a LIDAR sensor, or the like. Landmark features can be created or matched using motion sensor data, such as odometry or gyro data or the like, and adjacency sensor data. Despite the relative ambiguity of adjacency-sensor derived landmark features, a particle filter approach can be configured to use such information, instead of requiring “at-a-distance” information from a constant stream of visual images from a camera, such as for robot localization or mapping. Landmark sequence constraints or a Wi-Fi signal strength map can be used together with the particle filter approach.

Robot localization or mapping can be provided without requiring the expense or complexity of an “at-a-distance” sensor, such as a camera, a LIDAR sensor, or the like. Landmark features can be created or matched using motion sensor data, such as odometry or gyro data or the like, and adjacency sensor data. Despite the relative ambiguity of adjacency-sensor derived landmark features, a particle filter approach can be configured to use such information, instead of requiring “at-a-distance” information from a constant stream of visual images from a camera, such as for robot localization or mapping. Landmark sequence constraints or a Wi-Fi signal strength map can be used together with the particle filter approach.

Provided are an imaging element, a distance measuring device, and an electronic device capable of improving resolution of a distance image while preventing generation of electromagnetic noise.

An imaging element according to the present disclosure includes: a signal generator configured to generate a clock signal; a plurality of flip-flops connected in a cascade manner; a circuit block configured to supply a first signal to a clock terminal of each of the plurality of flip-flops and to supply a second signal to an input terminal of a first-stage flip-flop of the plurality of flip-flops in accordance with the clock signal; and a pixel array including pixels configured to be driven using pulse signals supplied from different stages of the plurality of flip-flops.

The present disclosure relates to a signal processing device that enables detection of the distance between an imaging device and a subject using an imaging device with high versatility, a signal processing method, and a program. A determination part classifies pixels to a plurality of pixel groups, and determines a pair of a first pixel group and a second pixel group using for detection of distance between the imaging device and the subject from a plurality of pixel groups on the basis of a charge accumulation period for each pixel group of the imaging device in which charge accumulation period is controlled, and a light projection period of pulse light projected toward the subject of the imaging device, for each pixel group. The present disclosure can be applied to, for example, a distance detection device or the like.

Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.