Brain Corporation

A method for localizing a mobile robot using a feature-embedded map includes detecting a power-on event of the robot at an unknown location, capturing a first image via a camera mounted on the robot, and encoding the first image into a first feature embedding vector using an image encoder. The method accesses a computer-readable map comprising a plurality of feature embedding vectors generated from images previously captured by the robot during prior navigation, where each feature embedding vector is stored at a respective physical location coordinate. The method determines similarity between the first feature embedding vector and the plurality of feature embedding vectors, identifies matching vectors exceeding a similarity threshold, and localizes the robot by correlating the unknown location with the physical location coordinates of the matching vectors. This enables autonomous navigation without QR codes, barcodes, or structural landmarks.

Systems and methods for detecting, mapping, and route planning around cliffs for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot processing a three-dimensional range sensor scan may utilize pre-computed neighboring points to detect cliffs, navigable ramps, and holes in a plane of a map used by the robotic device to navigate.

Systems and methods for detecting, mapping, and route planning around cliffs for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot processing a three-dimensional range sensor scan may utilize pre-computed neighboring points to detect cliffs, navigable ramps, and holes in a plane of a map used by the robotic device to navigate.

Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Systems and methods for localizing devices using network signatures and coverage maps measured by robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may generate a coverage map based on measurements collected during operation of the robot. The coverage map generated by the robot may be temporally accurate such that a device may be localized within the coverage map based on a received network signature from the device. The network signature comprising a measure of amplitudes of Wi-Fi networks and/or cellular networks at a point within an environment of the coverage map.

Systems, apparatuses, and methods for calibrating LiDAR sensors of a robot using intersecting LiDAR sensors are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may calibrate a calibration LiDAR based on a determined pose of the calibration LiDAR, wherein the pose is determined based on a measurement error between the calibration LiDAR and an intersecting reference LiDAR.

Systems and methods for enhancing task performance and computer readable maps produced by robots using modular sensors is disclosed herein. According to at least one non-limiting exemplary embodiment, robots may perform a first set of tasks, wherein coupling one or more modular sensors to the robots may configure a robot to perform a second set of tasks, the second set of tasks includes the first set of tasks and at least one additional task.

Systems and methods for laser and imaging odometry for autonomous robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may utilize images captured by a sensor and encoded with a depth parameter to determine its motion and localize itself The determined motion and localization may then be utilized to verify calibration of the sensor based on a comparison between motion and localization data based on the images and motion and localization data based on data from other sensors and odometry units of the robot.

Systems and methods for editing routes for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, Bezier curves and controls are provided to enable a user to intuitively manipulate a robot route, including changing the shape of the route, deleting segments of the route, and/or adding new route segments.

Systems, apparatuses, and methods for a distributed network of data collection and insight generation by server are disclosed herein. According to at least one non-limiting exemplary embodiment, the server may be configured to receive data from a network of data sources, receive an application from an application creator, and execute the application based on the data from the network of data sources to generate at least one insight, wherein the network of data sources may comprise mobile robots, stationary devices, IoT (Internet of Things) devices, and/or public data sources. The at least one insight may be utilized by robots to improve efficiency of operation or by humans to gain useful insights to the environment in which the data sources operate.