The present invention relates to a multi-channel lidar sensor module capable of measuring at least two target objects using one image sensor. The multi-channel lidar sensor module according to an embodiment of the present invention includes at least one pair of light emitting units configured to emit laser beams and a light receiving unit formed between the at least one pair of emitting units and configured to receive at least one pair of reflected laser beams which are emitted from the at least one pair of light emitting units and reflected by target objects.

An AI robot for cleaning in consideration of a user's action includes a camera to acquire a first image data for the user, a cleaning unit including a suction unit and a mopping unit, a driving unit configured to drive the AI robot, and a processor to determine the user's action using the first image data, determine a cleaning schedule in consideration of the user's action, and control the cleaning unit and the driving unit based on the determined cleaning schedule.

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

A method for image-guided agriculture includes receiving images; processing the images to generate reflectance maps respectively corresponding to spectral bands; synthesizing the reflectance maps to generate a multispectral image including vegetation index information of a target area; receiving crop information in regions of the target area; and assessing crop conditions for the regions based on the identified crop information and the vegetation index information.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

A computer-executable method relating to weeds, and a computer system. The method comprises: receiving an image (S11); recognizing one or more plants in the image in order to obtain the classification and/or names of the plants, and determining whether the plants are weeds (S12); and in response to determining that at least one plant is a weed, outputting information indicating that the at least one plant is a weed (S13).

A hearing aid and related systems and methods. In one implementation, a hearing aid system may comprise a wearable camera configured to capture images from an environment of a user, a microphone configured to capture sounds from the environment of the user, and a processor. The processor may be programmed to receive images captured by the camera; receive audio signals representative of sounds captured by the microphone; operate in a first mode to cause a first selective conditioning of a first audio signal; determine, based on analysis of at least one of the images or the audio signals, to switch to a second mode to cause a second selective conditioning of the first audio signal; and cause transmission of the first audio signal selectively conditioned in the second mode to a hearing interface device configured to provide sound to an ear of the user.

There is provided a method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of the image, a depth value, performing a local background enclosure calculation on the input image to determine salient object information, and generating a visual stimulus to visualise the salient object information using the visual stimulation device. Determining the salient object information is based on a spatial variance of at least one of the multiple points of the image in relation to a surface model that defines a surface in the input image.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.