A marine vessel management system, comprising: receiving input data comprising at least radar input data indicative of a first field of view and imagery input data indicative of a second field of view being at least partially overlapping with said first field of view. Processing the input data to determine data indicative of reflecting object(s) within an overlapping portion of said first field of view. Determining respective locations(s) within said second field of view, where said reflecting object(s) are identified, and obtaining radar meta-data of said reflecting object(s); processing said input imagery data said respective locations in an overlapping portion of said second field of view. Determining image data piece(s) corresponding with section(s) of said imagery data associated with said reflecting object(s). Using said radar meta-data for generating label data and generating output data comprising said image data section(s) and said label data.

Systems and methods are described for reacting to a feature in an environment of a robot based on a classification of the feature. A system can detect the feature in the environment using a first sensor on the robot. For example, the system can detect the feature using a feature detection system based on sensor data from a camera. The system can detect a mover in the environment using a second sensor on the robot. For example, the system can detect the mover using a mover detection system based on sensor data from a lidar sensor. The system can fuse the data from detecting the feature and detecting the mover to produce fused data. The system can classify the feature based on the fused data and react to the feature based on classifying the feature.

Provided is a channel monitoring method, an electronic device, and a storage medium. The method includes: obtaining scan data collected by a vehicle body collection component of an automated guided vehicle (AGV) in an area around a vehicle body; obtaining video data collected by a camera in a video collection area, where the camera is one of a plurality of cameras and is used to collect video of at least a partial area of the channel to be monitored of the plurality of channels to be monitored; in a case of determining an existence of a target object based on the scan data collected by the vehicle body collection component and/or the video data collected by the camera, obtaining a target channel where the target object is located; and generating target warning information for the target channel where the target object is located.

A UAV (unmanned aerial vehicle) including a first barometer and a processing unit is provided. The first barometer provides a first air pressure value. The processing unit is coupled to the first barometer for receiving the first air pressure value from the first barometer, performing timing-synchronization on the first air pressure value provided by the first barometer and an external reference air pressure value provided by an external reference barometer to obtain a timing-synchronized first air pressure value and recalculating the timing-synchronized first air pressure value to generate a compensated air pressure value, wherein the processing unit performs data fusion calculation on the first air pressure value, the compensated air pressure value and a sensor data to obtain a target fused data and real-timely controls the altitude and the posture of the UAV according to the target fused data.

Disclosed is an autonomous mobile service robot system for recognizing an automatic door and, in more detail, an autonomous mobile service robot system for recognizing an automatic door, the autonomous mobile service robot system enabling an autonomous mobile service robot to recognize an automatic door during moving, pass through the automatic door without an error in determination, and autonomously drive safely and stably for services by recognizing and defining information about an automatic door in simultaneously localization map-building (SLAM) of an autonomous mobile service robot that is operated for multiple purposes so that an accurate map considering location information of the automatic door is built.

Provided is a channel monitoring method, an electronic device, and a storage medium. The method includes: obtaining scan data collected by a vehicle body collection component of an automated guided vehicle (AGV) in an area around a vehicle body; obtaining video data collected by a camera in a video collection area, where the camera is one of a plurality of cameras and is used to collect video of at least a partial area of the channel to be monitored of the plurality of channels to be monitored; in a case of determining an existence of a target object based on the scan data collected by the vehicle body collection component and/or the video data collected by the camera, obtaining a target channel where the target object is located; and generating target warning information for the target channel where the target object is located.

A control method is provided, including: obtaining a current altitude of an aircraft, and determining a preset horizontal deviation threshold corresponding to the current altitude; obtaining a current horizontal deviation, wherein the current horizontal deviation is a horizontal deviation between a landing position and a current position of the aircraft; and determining a landing strategy of the aircraft based at least in part on a comparison between the current horizontal deviation and the preset horizontal deviation threshold.

An information processing apparatus according to an embodiment of the present technology includes: a calculation unit. The calculation unit calculates a self-position of an own device that moves with a moving object, in accordance with a first movement state of a moving object and a second movement state of the own device, on a basis of first movement information relating to the moving object and second movement information relating to the own device. As a result, it is possible to improve detection accuracy. Further, it is possible to improve the accuracy and reliability of the self-position. Since no displacement of the self-position occurs even in a movement space, it is possible for a drone flying in the air to avoid collision with obstacles in the movement space.

Systems and methods for vehicle localization are provided for a robotic vehicle, such as an autonomous mobile robot. The vehicle can be configured with multiple localization modes used for localization and/or pose estimation of the vehicle. In some embodiments, the vehicle comprises a first set of exteroceptive sensors and a second set of exteroceptive sensors, each being used for a different localization modality. The vehicle is able to disregard at least one localization modality for a number of different reasons, e.g., the disregarded location modality is adversely affected by the environment, to use less than the full complement of localization modalities to continue to stably localize the vehicle within an electronic map. In some embodiments, a localization modality may be disregarded for pre-planned reasons.

A machine learning approach can be used such as to synthesize geomagnetic maps having enhanced resolution versus lower resolution survey data. An on-board magnetometer can be used to measure a local magnetic field intensity, and a measured magnetic field intensity can be compared to the enhanced-resolution geomagnetic map. An indicium of a position of a vehicle on the enhanced-resolution geomagnetic map can be used, along with other sensor data, to provide an enhanced position estimate (or more generally, a state variable estimate) using a particle filtering technique supported by a deep reinforcement learning approach. Such a geomagnetic-aided navigation approach can be robust and need not rely on other navigational aids such as Global Navigation Satellite System (GNSS) or terrestrial transmitters.