The present technology provides systems, methods, and devices that can update aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle for these updates. As the autonomous vehicle navigates the route, data captured by at least one sensor of an autonomous vehicle can indicate an inconsistency between pre-mapped from a high-resolution sensor system describing a location on a map, and current data describing a new feature of the location. The current data can be clustered together based on a threshold spatial closeness, where the clustering describes the new feature, and semantic labels of the pre-mapped data from the high-resolution sensor system can be updated based on the new feature described by the clustered current data.

This disclosure provides a method and apparatus for recognizing a parking space.

An aspect of the present disclosure provides a method, performed by an apparatus of a host vehicle, for recognizing a parking space, the method including: recognizing another vehicle and a pillar based on data acquired from a light detection and ranging (lidar) device; recognizing a parking slot marking based on an image captured by a camera; and generating a map of an indoor parking lot based on information on the another vehicle, the pillar, and the parking slot marking.

A method generates first points based on a first scan of an environment that includes one or more moving objects. The method transforms the first points into a first static frame, which includes removing one or more of the first points corresponding to the one or more moving objects. The method generates second points based on a second scan of the environment that includes the one or more moving objects. The method transforms the second points into a second static frame, which includes removing one or more of the second points corresponding to the one or more moving objects. The method combines the first static frame and the second static frame into an accumulated static frame, which has an increase in resolution compared with the first static frame. The method then loads the accumulated static frame into a point cloud.

A detector for object authentication includes first and second illumination sources. The first illumination source projects an illumination pattern including a plurality of illumination features onto a surface of an object. The second illumination source projects an illuminating light beam onto the object. The detector also includes an image capture device for determining a first image including a plurality of reflection features generated by the surface of the object in response to the illumination pattern and for determining a second image including two dimensional information associated with the surface of the object generated in response to the illuminating light beam. The detector also includes an evaluation device for evaluating the first image and the second image, identifying a geometrical feature of the object, determining a material property of the object, and comparing the two dimensional information to data stored in a database for authentication of the object.

A method includes: deriving a first absolute motion of the first optical sensor based on radial positions, azimuthal positions, radial distances, and radial velocities of points in a first cluster of points representing a first static reference surface in a first frame captured by the first optical sensor; deriving a second absolute motion of the second optical sensor based on radial positions, azimuthal positions, radial distances, and radial velocities of points in a first cluster of points representing a first static reference surface in a second frame captured by the second optical sensor; calculating a rotational offset between the first optical sensor and the second optical sensor based on the first absolute motion and the second absolute motion; and aligning a third frame captured by the first optical sensor with a fourth frame captured by the second optical sensor based on the rotational offset.

A scanning system includes a plurality of light detection and ranging (LiDAR) sensors, each LiDAR sensor of the plurality of LiDAR sensors being configured to generate a point cloud based on interactions of emitted light with a surrounding environment, and a processor configured to split a full 360° rotation of each LiDAR sensor into a plurality of slices including a first slice and a second slice, to generate a coherent fused point cloud by fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the first slice, and fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the second slice, to determine an action for the scanning system to take in response to the generation of the coherent fused point cloud, and to cause the scanning system to implement the action.

The present application discloses a positioning method for a movable platform, including: detecting, by a laser positioning system (LPS) mounted on the movable platform, a plurality of reflectors mounted on a target object, wherein the movable platform is moving; calculating in real-time, by the LPS, according to the current position information, relative positions of the plurality of reflectors with respect to the LPS; and obtaining, by the LPS, a relative position of the movable platform with respect to the target object based on the relative positions of the plurality of reflectors with respect to the LPS. The present application also discloses positioning system that performs the positioning method.

A distance-measuring apparatus includes a precision calculation section that calculates a precision for each pixel, the precision based on a relation among the amounts of the electric charges stored at a plurality of timings that respectively delay by certain phases from a timing of the emission of the measuring light, wherein the precision is outputted from the distance-measuring apparatus.

A system and method for measuring three-dimensional (3D) coordinate values of an environment is provided. The method including moving a 2D scanner through the environment. A 2D map of the environment is generated using the 2D scanner. A path is defined through the environment using the 2D scanner. 3D scan locations along the path are defined using the 2D scanner. The 2D scanner is operably coupled to a mobile base unit. The mobile base unit is moved along the path based at least in part on the 2D map and the defined path. 3D coordinate values are measured at the 3D scan locations with a 3D scanner, the 3D scanner being coupled to the mobile base unit.

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.