Systems and methods for updating an electronic map of a facility are disclosed. The electronic map includes a set of map nodes. Each map node has a stored image data associated with a position within the facility. The method includes collecting image data at a current position of a self-driving material-transport vehicle; searching the electronic map for at least one of a map node associated with the current position and one or more neighboring map nodes within a neighbor threshold to the current position; comparing the collected image data with the stored image data of the at least one of the map node and the one or more neighboring map nodes to determine a dissimilarity level. The electronic map may be updated based at least on the collected image data and the dissimilarity level. The image data represents one or more features observable from the current position.

An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.

A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

Disclosed is a mobile robot configured to cut lawn in a work area. The mobile robot may include a main body, a weight sensing sensor, an obstacle sensing sensor, a blade, and a processor. The mobile robot may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and perform communication with other electronic devices in a 5G communication environment. As a result, it is possible to enhance user convenience.

Disclosed herein are a method of localization using multi sensors and a robot implementing the same, the method including sensing a distance between an object placed outside of a robot and the robot and generating a first LiDAR frame by a LiDAR sensor of the robot while a moving unit moves the robot, capturing an image of an object placed outside of the robot and generating a first visual frame by a camera sensor of the robot, and comparing a LiDAR frame stored in a map storage of the robot with the first LiDAR frame, comparing a visual frame registered in a frame node of a pose graph with the first visual frame, determining accuracy of comparison's results of the first LiDAR frame, and calculating a current position of the robot by a controller.

A mobile cleaning robot that includes a drive system configured to navigate around an operational environment, a ranging device configured to communicate with other ranging devices of respective electronic devices that are in the operational environment, and processors in communication with the ranging device that are configured to receive a distance measurement from the respective electronic devices present in the operational environment, each distance measurement representing a distance between the mobile cleaning robot and a respective electronic device, tag each of the distance measurements with location data indicative of a spatial location of the mobile cleaning robot in the operational environment, determine spatial locations of each of the electronic devices in the operational environment, and populate a visual representation of the operating environment with visual indications of the electronic devices in the operating environment.

A robotic work tool system (200) for defining a working area perimeter (105). The robotic work tool system (200) comprises a robotic work tool (100) and a controller (210). The robotic work tool (100) comprises a position unit (175) and a sensor unit (170). The controller (210) is configured to receive, from the sensor unit (170), edge data indicating whether the robotic work tool (100) is located next to a physical edge (430). The controller (210) is further configured to control the robotic work tool (100) to travel along the physical edge (430) while the edge data indicating that the robotic work tool (100) is located next to the physical edge (430) and to receive, from the position unit (175), position data while the robotic work tool (100) is in motion. The controller (210) is configured to determine, based on the edge data and position data, positions representing the physical edge (430) and to define, based on the determined positions, at least a portion of the working area perimeter (105).

Techniques for determining distortion in a map caused by measurement errors are discussed herein. For example, such techniques may include implementing a model to estimate map distortion between the map frame and the inertial frame. Data such as sensor data, map data, and vehicle state data may be input into the model. A map distortion value output from the model may be used to compensate vehicle operations in a local region by approximating the distortion as linearly varying about the region. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory.

A method for dynamic loop closure in a mobile automation apparatus includes: obtaining mapping trajectory data defining a plurality of trajectory segments traversing a facility to be mapped; controlling a locomotive mechanism of the apparatus to traverse a current segment; generating a sequence of keyframes for the current segment using sensor data captured via a navigational sensor of the apparatus; and, for each keyframe: determining an estimated apparatus pose based on the sensor data and a preceding estimated pose corresponding to a preceding keyframe; and, determining a noise metric defining a level of uncertainty associated with the estimated pose relative to the preceding estimated pose; determining, for a selected keyframe, an accumulated noise metric based on the noise metrics for the selected keyframe and each previous keyframe; and when the accumulated noise metric exceeds a threshold, updating the mapping trajectory data to insert a repetition of one of the segments.

The disclosure relates to a method for mapping a processing area, in particular for determining a processing area, as part of a navigation method for autonomous robot vehicles. According to the disclosure, said method is characterized in that boundary lines between adjoining mapped and unmapped subareas of the processing area that is to be mapped are identified by comparing distances traveled by the robot vehicle during an initial mapping trip within the processing area, mapping of an unmapped subarea adjoining a boundary line is initiated from a point on one of those identified boundary lines during another mapping trip of the robot vehicle into the unmapped subarea, and a map of the processing area is created on the basis of the subareas mapped by the robot vehicle.