Provided herein are a method of extracting a feature from an image using a laser pattern and an identification device and a robot including the same, and the identification device for extracting a feature from an image using a laser pattern, which includes a first camera coupled to a laser filter and configured to generate a first image including a pattern of a laser which is reflected from an object, a second camera configured to capture an area overlapping an area captured by the first camera to generate a second image, and a controller configured to generate a mask for distinguishing an effective area using the pattern included in the first image and extract a feature from the second image by applying the mask to the second image.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a location of a particular object relative to a vehicle. In one aspect, a method includes obtaining sensor data captured by one or more sensors of a vehicle. The sensor data is processed by a convolutional neural network to generate a sensor feature representation of the sensor data. Data is obtained which defines a particular spatial region in the sensor data that has been classified as including sensor data that characterizes the particular object. An object feature representation of the particular object is generated from a portion of the sensor feature representation corresponding to the particular spatial region. The object feature representation of the particular object is processed using a localization neural network to generate the location of the particular object relative to the vehicle.

A navigation system may include a processor programmed to receive, from a camera of the host vehicle, one or more images captured from an environment of the host vehicle, and analyze the one or more images to detect an indicator of an intersection. The processor may also be programmed to determine, based on output received from at least one sensor of the host vehicle, a stopping location of the host vehicle relative to the detected intersection, and analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of the host vehicle. The processor may further be programmed to send the stopping location of the host vehicle and the indicator of whether one or more other vehicles are in front of the host vehicle to a server for use in updating a road navigation model.

Systems, methods, and non-transitory computer-readable media can receive a geometric map and a semantic map associated with a geographic area, the semantic map comprising semantic data associated with vehicle navigation. A first semantic position estimate associated with a first piece of semantic data contained in the semantic map is generated based on semantic data location information associated with the first piece of semantic data. A final position for the first semantic position estimate is received. One or more three-dimensional semantic labels are applied to the geometric map based on the final position of the first semantic position estimate. A warped semantic map is generated. Generating the warped semantic map comprises warping the semantic map based on the one or more three-dimensional semantic labels.

An interactive method for a movable platform, an interactive system, a movable platform and a storage medium including the interactive method. The interactive method may include projecting three-dimensional point cloud data collected by a sensor into image data collected by a camera for fusion processing to obtain a fused image; rendering the fused image to determine a three-dimensional visualization image of a surrounding environment where the movable platform is located; and outputting the three-dimensional visualization image of the surrounding environment where the movable platform is located on a display interface.

A display method is applied to electronic equipment including a display assembly. A display content displayed by the display assembly includes a background and an object for reading located on the background. The display method includes: differentiating grayscales of background pixels of the background; and displaying the background based on the background pixels with the differentiated grayscales, and displaying the object for reading.

A system detects multiple instances of an object in a digital image by receiving a two-dimensional (2D) image that includes a plurality of instances of an object in an environment. For example, the system may receive the 2D image from a camera or other sensing modality of an autonomous vehicle (AV). The system uses a first object detection network to generate a plurality of predicted object instances in the image. The system then receives a data set that comprises depth information corresponding to the plurality of instances of the object in the environment. The data set may be received, for example, from a stereo camera of an AV, and the depth information may be in the form of a disparity map. The system may use the depth information to identify an individual instance from the plurality of predicted object instances in the image.

A system and method generate feature space data that may be used for object detection. The system includes one or more processors and a memory. The memory may include one or more modules having instructions that, when executed by the one or more processors, cause the one or more processors to obtain a two-dimension image of a scene, generate an output depth map based on the two-dimension image of the scene, generate a pseudo-LIDAR point cloud based on the output depth map, generate a bird's eye view (BEV) feature space based on the pseudo-LIDAR point cloud, and modify the BEV feature space to generate an improved BEV feature space using feature space neural network that was trained by using a training LIDAR feature space as a ground truth based on a LIDAR point cloud.

A method and system for generating a composite video for display in a vehicle. A plurality of video streams are generated from a plurality of video cameras configured to be positioned on the vehicle. The video streams are transformed by an electronic processor to create a virtual camera viewpoint. The transformed video streams are combined to generate a composite video including a portion of a first image that is generated from a first one of the video cameras. The electronic processor detects an object external to the vehicle and determines whether the object at least partially obscures the portion of the first image. When the object at least partially obscures the portion of the first image, the electronic processor supplements the portion of the first image with a portion of a second image that is generated by a second one of the video cameras.

The present technology relates to an information processing apparatus, an information processing method, a program, a mobile-object control apparatus, and a mobile object that make it possible to appropriately set the accuracy in detecting an object. An information processing apparatus includes a first object detector that performs an object detection on the basis of first sensor data from a first sensor; a second object detector that performs an object detection on the basis of second sensor data from a second sensor that differs in type from the first sensor; a tracking section that predicts a state of a target object that is a tracking target, on the basis of a result of the object detection performed on the basis of the first sensor data, and on the basis of a result of the object detection performed on the basis of the second sensor data; and a detection accuracy controller that sets a high-resolution range on the basis of the state of the target object that is predicted on the basis of the result of the object detection performed on the basis of the first sensor data, and on the basis of the result of the object detection performed on the basis of the second sensor data, the high-resolution range being a range in which an object detection is performed with a higher degree of accuracy than in a range other than the high-resolution range. The present technology is applicable to, for example, a system used to track a target object around a vehicle.