A method for mapping an environment by an electronic device is described. The method includes obtaining a set of sensor measurements. The method also includes determining a set of voxel occupancy probability distributions respectively corresponding to a set of voxels based on the set of sensor measurements. Each of the voxel occupancy probability distributions represents a probability of occupancy of a voxel over a range of occupation densities. The range includes partial occupation densities.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

According to one embodiment, provided is a method for controlling a system comprising a lawn mower robot, the method comprising: a boundary setting driving step wherein the lawn mower robot drives in order to set a boundary of a target work area in which at least three anchors are installed on the boundary thereof; a shadow area determination step wherein, in the boundary setting driving step, the lawn mower robot receives a signal from the anchors and sets, as a shadow area, an area where the signal cuts off; a driving ending step wherein when the lawn mower robot returns to an initial position, the boundary setting driving step is ended, and driving information received from the anchors is stored; an information transmission step for transmitting, to a simulator, the driving information and information on the shadow area and the target work area; an obstacle map generation step for generating, by the simulator, an obstacle map on the basis of the shadow area of each anchor; a screen output step wherein the simulator overlaps an externally provided map and the obstacle map, and outputs same on a screen; and an anchor recommending step for recommending, to a user, positions at which the size of the shadow areas identified within the target work area can be minimized. According to the present embodiment, a user can easily check whether the anchor installation positions are desirable.

Systems and methods for detecting an error in the mounting of a front hazard sensor are disclosed herein. According to at least one exemplary embodiment, an error in a pose of a front hazard sensor may comprise the front hazard sensor being orientated or positioned incorrectly with respect to a default pose. The present disclosure provides systems and methods for determining if this error in the pose is present.

A path planning method of mobile robots based on image processing is provided and includes: S1, preprocessing a map image: calculating a safety distance between a mobile robot and a surrounding obstacle during a movement of the mobile robot based on external geometric features of the mobile robot, forming a circular range on the map image with a expansion point as a center and the safety distance as an expansion radius to set a safety range, and marking the safety range; performing skeleton feature extraction on the map image after the marking to obtain a reference path map; S2, obtaining an initial path; and S3, optimizing the initial path. The path planning method improves the flexibility of the algorithm and has high robustness and operational efficiency, and the optimal path obtained can ensure the moving safety of the mobile robot.

An apparatus and the like for controlling a mobile body that are capable of adjusting a detection result by a radar device in accordance with a three-dimensional shape for each region of a three-dimensional map generated from an image captured by an image-capturing device are provided. A mobile body control unit 105 is an apparatus for controlling the vehicle (mobile body) including an image-capturing device 101 and a millimeter wave radar device 102 (radar device). A three-dimensional map generation unit 203 generates a three-dimensional map around the vehicle from an image captured by the image-capturing device 101. A radar weight map estimation unit 204 (weight estimation unit) estimates the weight of the detection result by the millimeter wave radar device 102 for each region of the three-dimensional map from the three-dimensional shape for each region of the three-dimensional map. A weight adjustment unit 205 (adjustment unit) adjusts a detection result by the millimeter wave radar device 102 on the basis of a weight.

This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.

Systems and methods for route synchronization between two or more robots to allow for a single training run of a route to effectively train multiple robots to follow the route.

A robot device includes: a sensor configured to generate sensing data related to an action of the robot device; a communication interface configured to communicate with a server; a memory storing instructions; and a processor configured to execute the instructions to: based on the action of the robot device changing, store action data in the memory, the action data including instruction data corresponding to the action, the sensing data related to the action, and map data related to the action, transmit, to the server via the communication interface, the action data stored in the memory, receive, from the server via the communication interface, threshold data corresponding to the action, and based on identifying that the sensing data is outside of a threshold range based on the threshold data received from the server, generate an event.

An autonomous robotic exploration method based on a reduced approximated generalized Voronoi graph, the method including: 1) constructing a reduced approximated generalized Voronoi topological map based on a morphological method; 2) obtaining an Next-Best-View and planning a global path from the robot to the Next-Best-View; and 3) navigating to the Next-Best-View along the global path R={r.sub.0, r.sub.1, r.sub.2, . . . , p.sub.NBV} based on a visual force field (VFF) algorithm.