Abstract: A storage medium, a robot, and a method for generating navigation map are provided. By disposing a first lidar and a second lidar located higher than the first lidar, it constructs a first map corresponding to the first lidar based on first laser data collected by the first lidar, and calculate second positioning data corresponding to the second lidar during constructing the first map, constructs a second map corresponding to the second lidar based on the second positioning data and second laser data collected by the second lidar, and obtains a navigation map corresponding to the robot by fusing the first map with the second map, such that the fused map includes not only positioning information provided by the first map, but also obstacle information provided by the first map and the second map.

Abstract: A robot navigation method, an electronic device, and a computer-readable storage medium are provided. The method includes: obtaining an initial map marked with an area type of each of obstacle areas in the initial map; obtaining, during navigating the robot according to the initial map, a real-time ultra-wideband localization position; determining, based on the real-time ultra-wideband localization position, a target obstacle area among the obstacle areas where the robot is located; and controlling, based on a navigation parameter corresponding to a target area type of the target obstacle area, the robot to move. In this manner, the safety of the robot can be improved.

Abstract: Examples of the present disclosure disclose a memory and an operation method thereof, a trim register, a memory system, and an electronic device. The memory includes: a pre-charge circuit configured to: pre-charge a read node based on a pre-charge signal; a trim register coupled to the read node and configured to: output trim information latched in the trim register based on a read enable signal; and select whether to discharge the read node based on the trim information; and a read output circuit coupled to the read node and configured to: generate a read output signal based on a read output enable signal and a level of the read node, wherein the read output signal is to indicate whether the trim information is correct.

Abstract: An indoor map construction method includes: after a robot enters a mapping mode, acquiring initial point cloud data at a starting position; extracting, from the initial point cloud data, a first principal direction; after rotating the initial point cloud data according to the first principal direction, controlling the robot to travel a preset distance so as to acquire updated point cloud data; extracting a second principal direction from the updated point cloud data; when the first principal direction is different from the second principal direction, rotating the acquired point cloud data according to the second principal direction and controlling the robot to continue traveling, until indoor global point cloud data are obtained; extracting a third principal direction from the global point cloud data; and when the second principal direction is different from the third principal direction, rotating the global point cloud data according to the third principal direction.

Abstract: A point cloud data processing method, an electronic device, and a computer-readable storage medium are provided. The method includes: obtaining a plurality of obstacle point clouds, and extracting wall surface point cloud(s) from the plurality of obstacle point clouds; obtaining a first point cloud vector by projecting the obstacle point clouds in the plurality of obstacle point clouds other than the wall surface point cloud(s) to a ground surface; projecting the wall surface point cloud(s) to the ground surface, and obtaining a straight line by performing a linear fitting on the projected wall surface point cloud(s); obtaining a second point cloud vector by screening the projected wall surface point cloud(s) based on the straight line; and combining the first point cloud vector and the second point cloud vector into along-edge semantic information. Through present disclosure, more accurate along-edge semantic information can be obtained.

Abstract: A method for determining a pose of a robot having a lidar including: obtaining a first pose of the robot in a map coordinate system; determining first positions of laser points corresponding to the lidar in the map coordinate system according to the first pose when the lidar performs laser scanning; determining matching scores between the first positions and grids where the first positions are located according to the first positions and mean values of the grids where the first positions are located, wherein the grids are grids in a probability map corresponding to the map coordinate system; determining a first confidence level for the first pose based on the matching scores; and determining a target pose according to the first confidence level.

Abstract: A mapping method, a computer-readable storage medium, and a robot are provided. The method is applied to a robot including a first lidar and a second lidar, where the first lidar is installed at a position higher than that of the second lidar. The method includes: obtaining a first laser key frame; calculating a current pose of a robot based on the first laser key frame; updating a first probability map based on the current pose and the first laser key frame; obtaining a second laser key frame; updating a second probability map based on the current pose and the second laser key frame; and generating a fused grid map for navigating the robot based on the updated first probability map and the updated second probability map, thereby greatly improving the stability of positioning and navigation.

Abstract: Examples of the present disclosure disclose a memory device, a trim register, a memory system, and an electronic apparatus. The memory device includes a programmable memory circuit configured to store a plurality of trim information; and a first trim register coupled with the programmable memory circuit and including a dynamic latch circuit, wherein the first trim register is configured to load a first trim information of the plurality of trim information from the programmable memory circuit in response to the memory device entering a working mode; and latch the first trim information to the dynamic latch circuit.

Abstract: A method for determining positions of navigation target points includes: acquiring an initial map, wherein the initial map represents a plan view corresponding to an explored area; performing contour extraction processing on the explored area to obtain a first contour map corresponding to the explored area, wherein the first contour map includes one or more connecting sections, and the one or more connecting sections are between adjacent ones of a number of sub-areas in the explored area, respectively; performing dilation processing on the first contour map to obtain a second contour map to remove the connecting sections; based on the initial map and the second contour map, extracting at least one navigation target point area from the explored area; and determining the positions of the navigation target points based on connected components corresponding to the at least one navigation target point area.

Abstract: A collided position determination method, a computer-readable storage medium, and a robot are provided. The method includes: obtaining, from a collision sensor of the robot, a triggered signal corresponding to a collision of the robot; determining at least two candidate positions of the collision based on the triggered signal corresponding to the collision; obtaining a motion trajectory of the robot after the collision; and obtaining a collided position by screening each of the candidate positions of the collision according to the motion trajectory. In this manner, when the robot collides during its movement, the collision signal generated by the collision sensor of the robot can accurately mark the collided position, thereby reducing the probability of the robot colliding again at the same position.

Abstract: A filtering method for a two-dimensional laser point cloud includes: acquiring a pitch angle and a roll angle of a mobile robot when the mobile robot is collecting a two-dimensional laser point cloud; determining a target tilt direction corresponding to the mobile robot and a target tilt angle corresponding to the two-dimensional laser point cloud according to the pitch angle and the roll angle; determining an angular filtering interval according to the target tilt direction and the target tilt angle, wherein the angular filtering interval is an angle interval corresponding to the laser point cloud to be filtered out; and filtering, by the mobile robot, the two-dimensional laser point cloud based on the angular filtering interval.

Abstract: A method for determining a pose of a robot having a lidar including: obtaining a first pose of the robot in a map coordinate system; determining first positions of laser points corresponding to the lidar in the map coordinate system according to the first pose when the lidar performs laser scanning; determining matching scores between the first positions and grids where the first positions are located according to the first positions and mean values of the grids where the first positions are located, wherein the grids are grids in a probability map corresponding to the map coordinate system; determining a first confidence level for the first pose based on the matching scores; and determining a target pose according to the first confidence level.

Abstract: A collided position determination method, a computer-readable storage medium, and a robot are provided. The method includes: obtaining, from a collision sensor of the robot, a triggered signal corresponding to a collision of the robot; determining at least two candidate positions of the collision based on the triggered signal corresponding to the collision; obtaining a motion trajectory of the robot after the collision; and obtaining a collided position by screening each of the candidate positions of the collision according to the motion trajectory. In this manner, when the robot collides during its movement, the collision signal generated by the collision sensor of the robot can accurately mark the collided position, thereby reducing the probability of the robot colliding again at the same position.

Abstract: A robot autonomous operation method, a robot, and a computer-readable storage medium are provided. The method includes: moving the robot, under a control of a user, along a guide path in an operation scene; generating a map including the guide path by positioning and mapping during the robot being moved along the guide path in the operation scene; generating a plurality of operation points on the guide path in the map; generating an operation path, wherein the operation path passes through all of the unpassed operation points and has a shortest total distance; and moving the robot, according to the operation path, to each of the unpassed operation points so as to perform an operation. In this manner, it controls the robot to explore the guide path in the operation scene by manual guiding, which can improve the exploration efficiency and reduce the risk of exploring unknown operation scenes.

Abstract: A storage medium, a robot, and a method for generating navigation map are provided. By disposing a first lidar and a second lidar located higher than the first lidar, it constructs a first map corresponding to the first lidar based on first laser data collected by the first lidar, and calculate second positioning data corresponding to the second lidar during constructing the first map, constructs a second map corresponding to the second lidar based on the second positioning data and second laser data collected by the second lidar, and obtains a navigation map corresponding to the robot by fusing the first map with the second map, such that the fused map includes not only positioning information provided by the first map, but also obstacle information provided by the first map and the second map.

Abstract: A mapping method, a computer-readable storage medium, and a robot are provided. The method is applied to a robot including a first lidar and a second lidar, where the first lidar is installed at a position higher than that of the second lidar. The method includes: obtaining a first laser key frame; calculating a current pose of a robot based on the first laser key frame; updating a first probability map based on the current pose and the first laser key frame; obtaining a second laser key frame; updating a second probability map based on the current pose and the second laser key frame; and generating a fused grid map for navigating the robot based on the updated first probability map and the updated second probability map, thereby greatly improving the stability of positioning and navigation.

Abstract: The present disclosure provides a robot visual image feature extraction method as well as an apparatus and a robot using the same. The method includes: collecting image data through visual sensor(s) of the robot, and collecting angular velocity data through inertial sensor(s) of the robot; calculating a relative pose between image frames in the image data based on the angular velocity data; extracting feature points of the first image frame in the image data; calculating a projection position of each feature point of the k-th image frame in the k+1-th image frame based on a relative pose between the k-th image frame and the k+1-th image frame; and searching for each feature point in the projection position in the k+1-th image frame, and performing a synchronous positioning and a mapping based on the searched feature point. In this manner, the feature points of dynamic objects are eliminated.

Abstract: The present disclosure relates to robot technology, and particularly to a robot localization method as well as an apparatus and a robot using the same. The method includes: obtaining a set of particles for localizing the robot; updating a position of each particle in the set of particles based on a preset motion model to obtain the updated position of the particle; obtaining laser measurement data and UWB measurement data; calculating a matching probability of each particle based on the laser measurement data, the UWB measurement data, and the updated position of the particle; and localizing the robot based on the matching probability of each particle. In such a manner, the UWB measurement data is applied to the traditional particle filtering localization algorithm based on laser measurement data so as to enhance the localization precision in large indoor scenes.

Abstract: The present disclosure provides a virtual rail based cruise method as well as an apparatus and a robot using the same. The method includes: obtaining a digital map including a virtual rail; performing a path planning based on the virtual rail, a current position of the robot, and a cruise end point to obtain a cruise path; and obtaining parameter(s) of the robot by calculating through a preset path tracking algorithm based on the cruise path and the current position of the robot, and controlling the robot based on the control parameter(s). In this manner, the problems of the prior art that needs to lay a rail or set an auxiliary device which causes high cost and inconvenience in usage as well as the rail needs to be re-laid or the auxiliary device needs to be reinstalled when the route is to be changed can be solved.

Abstract: The present disclosure provides a robot path planning method as well as an apparatus and a robot using the same. The method includes: obtaining a grid map and obtaining a position of obstacle and a position of track in the grid map; determining a cost of grids of the grid map based on the position of obstacle and the position of track; generating a grid cost map based on the cost of the grids and the grid map; and planning a global path of the robot from a current position to a destination position based on the grid cost map. In this manner, it effectively integrates free navigation and track navigation, thereby improving the flexibility of obstacle avoidance and ensuring the safety of obstacle avoidance of the robot.