A semi-automatic three-dimensional light detection and ranging (LIDAR) point cloud data annotation system and method for autonomous driving of a vehicle involve filtering 3D LIDAR point cloud and normalizing the filtered 3D LIDAR point cloud data relative to the vehicle to obtain normalized 3D LIDAR point cloud data, quantizing the normalized 3D LIDAR point cloud data by dividing it into a set of 3D voxels, projecting the set of 3D voxels to a 2D birdview, identifying a possible object by applying clustering to the 2D birdview projection, obtaining an annotated 2D birdview projection including annotations by a human annotator via the annotation system regarding whether the bounding box corresponds to a confirmed object and a type of the confirmed object, and converting the annotated 2D birdview projection to back into annotated 3D LIDAR point cloud data.

Aspects of the present disclosure involve systems, methods, and devices for fault detection in a Lidar system. A fault detection system obtains incoming Lidar data output by a Lidar system during operation of an AV system. The incoming Lidar data includes one or more data points corresponding to a fault detection target on an exterior of a vehicle of the AV system. The fault detection system accesses historical Lidar data that is based on data previously output by the Lidar system. The historical Lidar data corresponds to the fault detection target. The fault detection system performs a comparison of the incoming Lidar data with the historical Lidar data to identify any differences between the two sets of data. The fault detection system detects a fault condition occurring at the Lidar system based on the comparison.

An information processing device includes processing circuitry. The processing circuitry obtain target information that indicates at least one of a distance to a target object or a position of the target object. The processing circuitry generate, based on the target information, map information of a space including a plurality of areas, the map information indicating presence or absence of the target object in a first area included in the plurality of areas, and a detailed position of the target object in the first area.

A device includes an external sensor to scan an environment so as to periodically output scan data, a storage to store an environmental map, and a location estimation device to match the sensor data against the environmental map read from the storage so as to estimate a location and an attitude of the vehicle. The location estimation device determines predicted values of a current location and a current estimation of the vehicle in accordance with a history of estimated locations and estimated attitudes of the vehicle, and performs the matching by using the predicted values.

A work vehicle includes: an imaging device that captures an image in which a work target is shown; an image transmission unit that transmits the image captured by the imaging device to a control device; an operation signal reception unit that receives an operation signal from the control device; and an operation control unit that limits the operation signal according to a transmission status of the image.

A surveying device is operated to measure a surface profile of a terrain of a worksite and generate surface profile data indicative of the surface profile. A work machine is operated to move along a route over the terrain in accordance with an operating parameter and generates machine operational data indicative of the operating parameter. A navigation system determines the route and generates route data. A processing unit processes the route data, machine operational data and surface profile data to generate monitored operating condition data indicative of a monitored operating condition of the machine.

In one embodiment, a method for solution inference using neural networks in LiDAR localization includes constructing a cost volume in a solution space for a predicted pose of an autonomous driving vehicle (ADV), the cost volume including a number of sub volumes, each sub volume representing a matching cost between a keypoint from an online point cloud and a corresponding keypoint on a pre-built point cloud map. The method further includes regularizing the cost volume using convention neural networks (CNNs) to refine the matching costs; and inferring, from the regularized cost volume, an optimal offset of the predicted pose. The optimal offset can be used to determine a location of the ADV.

A self-moving device includes a body, a walking assembly arranged on the body, and a control system arranged in the body. The self-moving device further includes an optical receiving device and at least two optical emitting devices arranged on the body. Paths of emitted light emitted by the at least two optical emitting devices are different. The optical receiving device is adapted to receive a reflected light formed after the emitted light emitted by at least one of the optical emitting devices hits an obstacle. A distance measuring method of the self-moving device is also disclosed.

A line laser module, including: a module body; a first image capturing assembly, provided at the module body and comprising a first camera, at least one laser emitter and a first image processing module, wherein the at least one laser emitter is provided adjacent to the first camera and configured to emit a line laser with a linear projection toward outside of the module body, the first camera is configured to capture a first environment image containing the line laser, and the first image processing module is configured to acquire obstacle distance information based on the first environment image; and a second image capturing assembly, comprising a second camera and a second image processing module, wherein the second camera is configured to capture a second environment image, and the second image processing module is configured to acquire obstacle type information based on the second environment image.

Systems and methods provide a travel control system to augment a supplemental object detection system of a material handling vehicle. Speed limits can be calculated for material handling vehicles based on properties of the vehicle, and a field of view of the object detection systems of the material handling vehicle. For given steer angles or ranges of steer angles, the speed of the material handling vehicle can be limited to ensure that the vehicle could stop before contact with a newly-detected object that was previously outside the field of view of the object detection system.