A work machine measurement system includes an image acquisition unit that acquires an image of a work target captured, while a swinging body of a work machine is swinging, by an image capturing device mounted on the swinging body, a three-dimensional position calculation unit that calculates a three-dimensional position of the work target based on the image, a swing data acquisition unit that acquires swing data about the swinging body, a determination unit that determines whether or not the swing data satisfies a predefined swinging condition and an output unit that outputs a swinging instruction signal based on a result of the determination by the determination unit.

A work machine measurement system includes an image acquisition unit that acquires an image of a work target captured, while a swinging body of a work machine is swinging, by an image capturing device mounted on the swinging body, a three-dimensional position calculation unit that calculates a three-dimensional position of the work target based on the image, a swing data acquisition unit that acquires swing data about the swinging body, a determination unit that determines whether or not the swing data satisfies a predefined swinging condition and an output unit that outputs a swinging instruction signal based on a result of the determination by the determination unit.

Example implementations described herein are directed to depression detection on roadways (e.g., potholes, horizontal panel lines of a roadway, etc.) through using vision sensor to realize improved safety for advanced driver assistance systems (ADAS) and autonomous driving (AD). Example implementations described herein detect candidate depressions in the roadway in real time and adjust the control of the vehicle system according to the detected depressions.

Example implementations described herein are directed to depression detection on roadways (e.g., potholes, horizontal panel lines of a roadway, etc.) through using vision sensor to realize improved safety for advanced driver assistance systems (ADAS) and autonomous driving (AD). Example implementations described herein detect candidate depressions in the roadway in real time and adjust the control of the vehicle system according to the detected depressions.

In example embodiments, techniques are provided for enabling use of an AI-generated TIN in generation of a 3D design model by defining site objects (e.g., pads) using multiple (e.g., three) phases (i.e. states). A conceptual phase may be associated with a conceptual data structure, a preliminary phase may be associated with the conceptual data structure and a preliminary data structure, a final phase may be associated with the conceptual data structure, the preliminary data structure, and a final data structure. If changes are made in the conceptual phase, for example, as a result of AI optimization, they may be propagated up to the preliminary data structure and final data structure via the vertical draping. Changes made in the preliminary phase or final phase may be propagated down to the conceptual data structure by treating boundaries and breaklines as spatial constraints.

In example embodiments, techniques are provided for enabling use of an AI-generated TIN in generation of a 3D design model by defining site objects (e.g., pads) using multiple (e.g., three) phases (i.e. states). A conceptual phase may be associated with a conceptual data structure, a preliminary phase may be associated with the conceptual data structure and a preliminary data structure, a final phase may be associated with the conceptual data structure, the preliminary data structure, and a final data structure. If changes are made in the conceptual phase, for example, as a result of AI optimization, they may be propagated up to the preliminary data structure and final data structure via the vertical draping. Changes made in the preliminary phase or final phase may be propagated down to the conceptual data structure by treating boundaries and breaklines as spatial constraints.

Through discrimination of the scattered signal polarization state, a lidar system measures a distance through semi-transparent media by the reception of single or multiple scattered signals from a scattering medium. Combined and overlapped single or multiple scattered light signals from the medium can be separated by exploiting varying polarization characteristics. This removes the traditional laser and detector pulse width limitations that determine the system's operational bandwidth, translating relative depth measurements into the conditions of two surface timing measurements and achieving sub-pulse width resolution.

Through discrimination of the scattered signal polarization state, a lidar system measures a distance through semi-transparent media by the reception of single or multiple scattered signals from a scattering medium. Combined and overlapped single or multiple scattered light signals from the medium can be separated by exploiting varying polarization characteristics. This removes the traditional laser and detector pulse width limitations that determine the system's operational bandwidth, translating relative depth measurements into the conditions of two surface timing measurements and achieving sub-pulse width resolution.

Provided are a surveillance system, an information processing device, a fall detection method, and a non-transitory computer readable medium capable of improving the accuracy of detecting a person who has fallen down in a railroad crossing. A surveillance system according to the present disclosure includes a LiDAR sensor (10) that emits a plurality of laser beams with different ranges to a surveillance area, and outputs a detection signal indicating a detection status of an object by each of the laser beams, and an information processing device (20) that determines that the object has fallen down when the detection status by a laser beam with a shorter range than a predetermined range indicates that the object is detected and the number of laser beams detecting the object decreases.

Systems and methods for determining elevation based on structural modeling and light detection and ranging (LIDAR) data are disclosure. LIDAR bare earth data corresponding to an area within a parcel boundary is obtained as preliminary elevation data. A basis of structure boundary is determined for a structure within the parcel boundary based on an absence of the LIDAR bare earth data within a region in the area. Three-dimensional models are generated based on photographic data, to represent portions of the structure that affect LIDAR signals. A structure boundary for the structure is determined based on the basis of structure boundary in combination with supplemental elevation data generated using the three-dimensional models. Adjacent grade values are determined based on a portion of the preliminary elevation data and supplemental elevation data corresponding to an area between the structure boundary and a buffer boundary.