Methods, apparatuses, and embodiments related to a technique for monitoring construction of a structure. In an example, a robot with a sensor, such as a LIDAR device, enters a building and obtains sensor readings of the building. The sensor data is analyzed and components related to the building are identified. The components are mapped to corresponding components of an architect's three dimensional design of the building, and the installation of the components is checked for accuracy. When a discrepancy above a certain threshold is detected, an error is flagged and project managers are notified. Construction progress updates do not give credit for completed construction that includes an error, resulting in improved accuracy progress updates and corresponding improved accuracy for project schedule and cost estimates.

The disclosure relates to a time-of-flight camera comprising: a time-of-flight sensor having several time-of-flight pixels for determining a phase shift of emitted and captured light, distance values being determined in accordance with the detected phase shifts, characterised in that the time-of-flight camera has a memory in which parameters of a point spread function, which characterise the time-of-flight camera and the time-of-flight sensor, are stored; an evaluation unit which is designed to deploy a detected complex-valued image in Fourier space, in accordance with the stored point-spread function, and a complex-valued image corrected by diffused light is determined and the phase shifts or distance values are determined using the corrected complex-valued image.

In general, the present disclosure is directed to a time of flight (ToF) sensor arrangement that may be utilized by a robot (e.g., a robot vacuum) to identify and detect objects in a surrounding environment for mapping and localization purposes. In an embodiment, a robot is disclosed that includes a plurality of ToF sensors disposed about a housing of the robot. Two or more ToF sensors may be angled/aligned to establish overlapping field of views to form redundant detection regions around the robot. Objects that appear therein may then be detected by the robot and utilized to positively identify, e.g., with a high degree of confidence, the presence of the object. The identified objects may then be utilized as data points by the robot to build/update a map. The identified objects may also be utilized during pose routines that allow the robot to orient itself within the map.

A time-of-flight ranging system disclosed herein includes a receiver asserting a photon received signal in response to detection of light that has reflected off a target and returned to the time-of-flight ranging system. A first latch circuit has first and second data inputs receiving a first pair of differential timing references, the first latch circuit latching data values at its first and second data inputs to first and second data outputs based upon assertion of the photon received signal. A first counter counts latching events of the first latch circuit during which the first data output is asserted, and a second counter counts latching events of the first latch circuit during which the second data output is asserted. Processing circuitry determines distance to the target based upon counted latching events output from the first and second counters.

Provided are methods for enhanced semantic labeling in mapping with a semantic labeling system, which can include receiving, from a LiDAR sensor of a vehicle, LiDAR point cloud information including at least one raw point feature for a point, receiving, from a camera of the vehicle, image data associated with an image captured using the camera, generating at least one rich point feature for the point based on the image data, predicting, using a LiDAR segmentation neural network and based on the at least one raw point feature and the at least one rich point feature, a point-level semantic label for the point, and providing the point-level semantic label to a mapping engine to generate a map based on the point-level semantic label Systems and computer program products are also provided.

A portable sensor system is provided for detecting objects in the environment of the portable sensor system and for being temporarily attached to a mobile object. The portable sensor system includes an environment detection sensor for detecting objects, an attaching device connected to the environment detection sensor for temporarily attaching the portable sensor system to an external surface of the mobile object, and a position determining apparatus for determining a sensor position of the environment detection sensor when the environment detection sensor is attached to the external surface. The position determining apparatus is configured to determine the sensor position relative to the mobile object on the basis of a predetermined geometrical model of the external surface and a region of the external surface detected by the environment detection sensor.

An object detection device includes an irradiation unit, a light reception unit and a detection unit. The light reception unit is configured to receive reflected light of light radiated by the irradiation unit and environment light. The detection unit is configured to detect a predetermined object based on a point group that is information based on the reflected light and at least one image. The point group is a group of reflection points detected in the whole distance measurement area. The at least one image includes an environment light image that is an image based on the environment light, a distance image that is an image based on a distance to an object detected on a basis of the reflected light and/or a reflection intensity image that is an image based on reflection intensity of the reflected light.

The present invention relates to a 3D (three-dimensional) modelling method and system. The 3D modelling method comprises capturing a plurality of digital images of an object, producing respective point cloud for the object, interpreting the plurality of digital images and the point cloud of the object to obtain a data information associated with the object, and generating a 3D model of the object based on the plurality of digital images, the point cloud, and the data information.

A method and systems for controlling and directing a means of transportation to autonomously navigate to a target location are provided. The method includes receiving measurements from one or more sensors of the means of transportation; building a route based on the measurement received for the means of transportation to navigate to a target location; generating localization estimation associated with the route built; generating a global path based on the route and the localization estimation; and performing local planning for directing the means of transportation to the target location while avoiding surrounding static or dynamic obstacles. The one or more sensors include a LiDAR sensor and an odometry sensor.

A position determination device, including a main body, a first laser emitter disposed on at least a portion of the main body to emit a first laser beam pointing in a first direction, a second laser emitter hingedly disposed on at least a portion of the main body to emit a second laser beam pointing in the first direction, such that the first laser emitter and the second laser emitter determine at least one of a size of a conduit, an angle to install the conduit, a distance of the first laser emitter to a wall, and a distance of the second laser emitter to the wall, and a conduit attachment unit removably connected to at least a portion of the main body to connect the main body to the conduit.