A stereo pair camera system for depth estimation, including: a first camera disposed in a first position along a longitudinal axis, a lateral axis, and a vertical axis and having a first field of view; and a second camera disposed in a second position along the longitudinal axis, the lateral axis, and the vertical axis and having a second field of view; wherein the first camera is a of a first type and the second camera is of a second type that is different from the first type; and wherein the first field of view overlaps with the second field of view. Optionally, the first position is spaced apart from the second position along one or more of the longitudinal axis and the vertical axis. The depth estimation is used by one or more of a driver assist system and an autonomous driving system of a vehicle.

The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed on the ego vehicle to facilitate autonomous driving of the ego vehicle needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor, with a first position of the ego vehicle or a first configuration of the sensor with respect to the ego vehicle. When a response of the request is received, an assisting vehicle is indicated to travel to be near the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle and the ego vehicle coordinates with the assisting vehicle to enable the sensor to acquire information of a target present on the assisting vehicle for the collaborative calibration of the sensor.

A driver assistance system for an ego vehicle, and a method for a driver assistance system is provided. The system is configured to refine a coarse geolocation method based on the detection of the static features located in the vicinity of the ego vehicle. The system performs at least one measurement of the visual appearance of each of at least one static feature located in the vicinity of the ego vehicle. Using the at least one measurement, a position of the ego vehicle relative to the static feature is calculated. The real world position of the static feature is identified. The position of the ego vehicle relative to the static feature is calculated, which is, in turn, used to calculate a static feature measurement of the vehicle location. The coarse geolocation measurement and the the static feature measurement are combined to form a fine geolocation position. By combining the measurements, a more accurate location of the ego vehicle can be determined.

Provided in the present disclosure are a robot and a control method therefor. The robot includes: a depth camera; a driver; and a processor for acquiring a depth image by performing photographing through the depth camera, generating a plurality of 3D points on a three-dimensional (3D) space corresponding to a plurality of pixels, based on depth information about the plurality of pixels of the depth image, identifying a plurality of 3D points having a preset height value, based on a driving bottom surface of the robot in the 3D space from among the plurality of 3D points, and controlling the driver to move the robot based on the identified plurality of 3D points.

A vehicle control apparatus controls movement of a vehicle in a lateral direction intersecting a direction in which the vehicle travels based on a movement trajectory of a preceding vehicle. The vehicle control apparatus includes a detection unit configured to detect a surrounding environment of the vehicle, and a preceding vehicle which travels ahead in the same lane in which the vehicle travels, a determination unit configured to determine whether or not the preceding vehicle straddles lanes or approaches within a set distance predetermined for the lanes based on lateral movement information of the preceding vehicle detected by the detection unit, and a control unit configured to control lateral movement of the vehicle based on a determination result of the determination unit and detection information of the detection unit.

An autonomous farming machine navigable in an environment for performing farming action(s) is disclosed. The farming machine receives a notification from a manager that there are no obstacles in the blind spots of the detection system. The farming machine applies an obstacle detection model to the captured images to verify that there are no obstacles in unobstructed views. The farming machine determines a configuration of the farming machine. The farming machine determines a virtual safety bubble for the farming machine to autonomously perform the farming action(s) based on the determined configuration. The farming machine detects an obstacle in the environment by applying the obstacle detection model to the captured images. The farming machine determines that the obstacle is entering the virtual safety bubble. In response to determining that the obstacle is entering the virtual safety bubble, the farming machine terminates operation of the farming machine and/or enacts preventive measures.

Disclosed is a light and detection ranging (LiDAR) system for a vehicle, which includes: a laser generator generating an optical signal having an address signal and a pulse signal; and a plurality of LiDAR sensors connected to an optical fiber bus, in which each of the plurality of LiDAR sensors determines whether the pulse signal of the optical signal is received according to the address signal of the optical signal.

In one aspect, a vehicle is provided that includes i) a plurality of wheel-leg components and ii) a surround view imaging system for generating a surround view image of the vehicle. The plurality of wheel-leg components can operate to provide locomotion to the vehicle. The surround view image comprising a 360-degree, three-dimensional view of an environment surrounding the vehicle. The vehicle is configured to operate autonomously using the surround image view to control the locomotion of the plurality of the wheel-leg components.

An autonomous moving apparatus includes a moving unit moving the apparatus, a detector detecting distances from surrounding objects and shapes of the objects, and a controller. When a route of the apparatus is adjusted to a route of a follow target under control of the moving unit, the controller controls the moving unit so that the apparatus continues to move without changing the route of the apparatus if the follow target has changed the route but an obstacle having a possibility of causing a movement abnormality when the apparatus moves over the obstacle is present between the route of the follow target and the route of the apparatus. When the obstacle is no longer present between the route of the follow target and the route of the apparatus, the controller controls the moving unit so that the route of the apparatus is adjusted to the route of the follow target.

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.