A system provided for use with a lawn mower includes a processor in communication with the lawn mower. The processor is configured to acquire geographic location data of the lawn mower and mapping data for the lawn mower as the lawn mower operates on the plot of land. The processor is configured to acquire performance data of the lawn mower as the lawn mower operates on the plot of land according to a first path and generate a map of the plot of land at least partially based on the geographic location data and the mapping data. The processor is configured to determine an operational efficiency of the lawn mower according to the first path and determine a second path for the lawn mower that increases the operational efficiency of the lawn mower for operating on the plot of land.

An information processing apparatus according to an aspect of the present technology includes an estimation unit, a generation unit, and a frequency control unit. The estimation unit estimates at least one of a location or a posture of a mobile object. The generation unit generates a movement plan for moving the mobile object. The frequency control unit controls frequency of update of the movement plan to be performed by the generation unit, on the basis of load index information serving as an index of a load on the estimation unit.

A method capable of appropriately estimating (specifying) a self-position of a mobile object while appropriately correcting an estimated value of a self-position by an SLAM algorithm is provided. In a self-position estimation method, an actual self-position of a mobile object 1 is specified (fixed) from self-positions estimated by a plurality of algorithms. The plurality of algorithms includes an SLAM algorithm (12) and an algorithm (11) different from the SLAM algorithm. A correction processing unit 16 intermittently corrects an estimated value of a self-position obtained by the SLAM algorithm in accordance with any one self-position out of an estimated value of a self-position obtained by an algorithm other than SLAM and a specified self-position.

A method in a navigational controller includes: controlling a ceiling-facing camera of a mobile automation apparatus to capture a stream of images of a facility ceiling; activating a primary localization mode including: (i) detecting primary features in the captured image stream; and (ii) updating, based on the primary features, an estimated pose of the mobile automation apparatus and a confidence level corresponding to the estimated pose; determining whether the confidence level exceeds a confidence threshold; when the confidence level does not exceed the threshold, switching to a secondary localization mode including: (i) detecting secondary features in the captured image stream; (ii) updating the estimated pose and the confidence level based on the secondary features; and (iii) searching the image stream for the primary features; and responsive to detecting the primary features in the image stream, re-activating the primary localization mode.

In one embodiment, a method for synchronizing sensor data of an autonomous driving vehicle includes determining, by a processing device of an inertial navigation system (INS), that global navigation satellite system (GNSS) data is unavailable and identifying an alternative source of time information. The method further includes retrieving time information from the alternative source and synchronizing sensor data with the time information from the alternative source of time information.

Robot localization or mapping can be provided without requiring the expense or complexity an “at-a-distance” sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and docks based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

A robotic work tool system (200) for defining a working area (105) in which at least one robotic work tool (100) subsequently is intended to operate. The system (200) comprises at least one controller (210) being configured to receive sensor data for pose estimation and event data relating to a plurality of events of at least one robotic work tool (100) moving within the working area (105). The received sensor and event data are associated with each other in time. The controller (210) is configured to determine positions for the events based on the received sensor data associated with the respective event data and to determine features reflecting the working area (105) by relating positions associated with corresponding events with each other. The controller (210) is configured to adjust the determined positions based on the determined features by, for each feature, comparing the respective determined positions with each other; and determine, based on the adjusted positions, a map defining the working area (105).

An example method may include i) determining a first distance between a pair of feet of a robot at a first time, where the pair of feet is in contact with a ground surface; ii) determining a second distance between the pair of feet of the robot at a second time, where the pair of feet remains in contact with the ground surface from the first time to the second time; iii) comparing a difference between the determined first and second distances to a threshold difference; iv) determining that the difference between determined first and second distances exceeds the threshold difference; and v) based on the determination that the difference between the determined first and second distances exceeds the threshold difference, causing the robot to react.

Systems and methods for localization of a trailer of an autonomous tractor-trailer are described herein. Some implementations can determine a sector area in an environment of the autonomous tractor-trailer that is predicted to include the trailer, determine a subset of an LIDAR data that is generated by LIDAR sensor(s) of an autonomous tractor of the autonomous tractor-trailer and that is predicted to include the trailer based on the sector area, generate a trailer pose instance of a trailer pose of the trailer based on the subset of the LIDAR data, and cause the trailer pose instance to be utilized in controlling the autonomous tractor-trailer. Additional or alternative implementations can utilize particular LIDAR sensor(s) in generating the trailer pose instance, such as phase coherent LIDAR sensor(s) or polarized LIDAR sensor(s).

The present application relates to a multi-sensor-fusion-based autonomous mobile robot indoor and outdoor navigation method and a robot. The method includes: acquiring inertial measurement data and three-dimensional point cloud data of a robot at a current position; determining a pose change of the robot based on the inertial measurement data of the robot at the current position; performing distortion correction on the three-dimensional point cloud data of the robot at the current position based on the pose change of the robot; and matching the three-dimensional point cloud data after the distortion correction with a navigation map, to determine the current position of the robot. With the method, the robot can be accurately positioned.