A robot comprises a memory, a processor, a body and a drive system which are coupled. The drive system comprises one of auger-based surface interface portions and continuous tread surface interface portions. The auger-based surface interface portions and the continuous tread surface interface portions are interchangeable to adapt the robot to one of different operating conditions and different uses. The processor is configured to: control movement of the robot, via the drive system, to traverse across a first surface, wherein the first surface comprises piled granular material, in response to the drive system being configured with the auger-based surface interface portions; and control movement of the robot via the drive system to traverse across a second surface, which is a solid or semi-solid surface other than the piled granular material, in response to the drive system being configured with the continuous tread surface interface portions.

Provided is an autonomous flight system of a drone unit including an information acquisition unit configured to acquire first information through a camera mounted to the drone unit, acquire second information through LiDAR, and acquire third information through an inertial measurement unit (IMU); an information processing unit configured to estimate a pose of the drone unit through the second information and the third information, acquire a coordinate value of a target object through the first information and the second information, and construct a 3D map and an unknown area exploration algorithm based on a pose estimation value of the drone unit, the coordinate value of the target object, and the second information; a path planner configured to generate an obstacle avoidance path based on the 3D map and the unknown area exploration algorithm; and a flight controller configured to apply the obstacle avoidance path to the drone unit.

Various examples are provided related to generating a map of an environment. In one example, a method includes obtaining a coarse global mapping of an environmental space; determining a robotic traversal path within the environmental space using the coarse global mapping, the robotic traversal path maintaining a targeted distance to a nearest structure within the environmental space; initiating traversal of a robot along the determined robotic traversal path, the robot obtaining depth sensor measurements of the nearest structure during traversal along the determined robotic traversal path; and refining the robotic traversal path during traversal by the robot along the determined robotic traversal path based upon the depth sensor measurements, where the robotic traversal path is refined online to achieve targeted resolution and local accuracy of the depth sensor measurements. A refined map of the environmental space can be generated using the depth sensor measurements.

Systems, apparatuses and methods are provided herein for providing monitoring premises. In one embodiment, a system for monitoring premises comprises: an unmanned aerial vehicle (UAV) comprising a three dimension (3D) scanner, a baseline model database, and a control circuit comprising a communication device for communicating with the UAV. The control circuit being configured to: instruct the UAV to travel to a monitored premises and perform a 3D scan with the 3D scanner to obtain a 3D point cloud of the monitored premises, compare a current state of the one or more features in the 3D point cloud of the monitored premises with a baseline state in a baseline state model, and identify a deviation of the current state of the one or more features of the monitored premises from the baseline state.

Systems, apparatuses and methods are provided herein for providing surveying premises of a customer. In one embodiment, a system for surveying premises of a customer comprises: an unmanned aerial vehicle (UAV) comprising a three dimension (3D) scanner and an image sensor, and a control circuit comprising a communication device configured to communicate with the UAV. The control circuit being configured to: receive, from a customer, a premises location, instruct the UAV to travel to the premises location to collect a set of data, form a 3D point cloud model of the premises based on 3D data collected by the 3D scanner of the UAV, identify one or more features of the premises based on the 3D point cloud model and image data collected by the image sensor of the UAV, and generate a recommendation to the customer based on the one or more features of the premises.

A computer implemented method for updating a scene representation model is disclosed. The method comprises obtaining a scene representation model representing a scene having one or more objects, the scene representation model being configured to predict a value of a physical property of one or more of the objects; obtaining a value of the physical property of at least one of the objects, the obtained value being derived from a physical contact of a robot with the at least one object; and updating the scene representation model based on the obtained value. An apparatus is also disclosed.

A method for determining an ego pose of a mobile system and creating a surfel map of a surrounding area of the mobile system via an optimization problem represented by a factor graph includes the steps of: receiving environment sensor data generated by an environment sensor attached to the mobile system, wherein the environment sensor surveys the surrounding area of mobile system, and wherein the environment sensor data represent the surrounding area of the mobile system as a point cloud; generating surfels by converting the point cloud of the received environment sensor data into surfel data; identifying new surfels and known surfels in the generated surfels by comparing the surfel data with the surfel map; and adding a surfel factor for the known surfels to the factor graph and/or adding a surfel node and a surfel factor for the new surfels to the factor graph.

A construction vehicle generates an object map to facilitate navigation. The construction vehicle captures an image of the area behind the construction vehicle, and generates a disparity image reflecting depth behind the construction vehicle. The construction vehicle generates and processes a 3D point cloud representation of the area behind the construction vehicle to identify a ground plane. The construction vehicle dynamically generates a virtual plane parallel to the ground plane based on the movement and position of the construction vehicle. After applying the virtual plane to the disparity image, the construction vehicle generates an object map identifying locations of objects greater than a threshold size and the height of the virtual plane. The construction vehicle navigates through the area behind the construction vehicle using the generated object map.

A map generation method including receiving a map editing request for a specific floor among a plurality of floors of a building, providing an editing interface on a display unit of an electronic device in response to the map editing request, the editing interface including at least a part of a specific map corresponding to the specific floor, specifying at least one node group allocatable on the specific map based on first node rules, the first node rules corresponding to spatial characteristics of the specific floor, and performing a node placement process such that first nodes included among the at least one node group are placed on the specific map.

A method of generating an environmental model of an environment (24) in an industrial plant or logistics plant is provided, wherein a plurality of sensors (18) distributed over the environment (24) detect a respective local partial zone of the environment (24) and the environmental model is assembled therefrom, In this respect, a plurality of autonomous mobile reconnaissance units (12), in particular autonomous mobile robots; move in the environment (24) and at least some of the sensors (18) are part of a mobile reconnaissance unit (12) and thus the environment (24) at changing locations.