The present invention provides a loop closure detection method and system, a multi-sensor fusion SLAM system, a robot, and a medium. Said system runs on a mobile robot, and comprises a similarity detection unit, a visual pose solving unit, and a laser pose solving unit. According to the loop closure detection system, the multi-sensor fusion SLAM system and the robot provided in the present invention, the speed and accuracy of loop closure detection in cases of a change in a viewing angle of the robot, a change in the environmental brightness, a weak texture, etc. can be significantly improved.

The present invention provides a loop closure detection method and system, a multi-sensor fusion SLAM system, a robot, and a medium. Said system runs on a mobile robot, and comprises a similarity detection unit, a visual pose solving unit, and a laser pose solving unit. According to the loop closure detection system, the multi-sensor fusion SLAM system and the robot provided in the present invention, the speed and accuracy of loop closure detection in cases of a change in a viewing angle of the robot, a change in the environmental brightness, a weak texture, etc. can be significantly improved.

An information processing apparatus according to an embodiment of the present technology includes a line-of-sight estimator, a correction amount calculator, and a registration determination section. The line-of-sight estimator calculates an estimation vector obtained by estimating a direction of a line of sight of a user. The correction amount calculator calculates a correction amount related to the estimation vector on the basis of at least one object that is within a specified angular range that is set using the estimation vector as a reference. The registration determination section determines whether to register, in a data store, calibration data in which the estimation vector and the correction amount are associated with each other, on the basis of a parameter related to the at least one object within the specified angular range.

This application provides an image processing method, a network training method, and a related device, and relates to image processing technologies in the artificial intelligence field. The method includes: inputting a first image including a first vehicle into an image processing network to obtain a first result output by the image processing network, where the first result includes location information of a two-dimensional 2D bounding frame of the first vehicle, coordinates of a wheel of the first vehicle, and a first angle of the first vehicle, and the first angle of the first vehicle indicates an included angle between a side line of the first vehicle and a first axis of the first image; and generating location information of a three-dimensional 3D outer bounding box of the first vehicle based on the first result.

This application provides an image processing method, a network training method, and a related device, and relates to image processing technologies in the artificial intelligence field. The method includes: inputting a first image including a first vehicle into an image processing network to obtain a first result output by the image processing network, where the first result includes location information of a two-dimensional 2D bounding frame of the first vehicle, coordinates of a wheel of the first vehicle, and a first angle of the first vehicle, and the first angle of the first vehicle indicates an included angle between a side line of the first vehicle and a first axis of the first image; and generating location information of a three-dimensional 3D outer bounding box of the first vehicle based on the first result.

A map information update method includes: (a) obtaining map information; (b) obtaining landmark observed positions indicating positions of one or more landmarks in a captured image; (c) adding that includes (i) generating added map information by adding information pertaining to the landmark observed positions to the map information, and (ii) updating the map information obtained in (a) to the added map information; (d) predicting that includes (i) calculating predicted map information based on the map information updated in (c), by using a neural network inference engine that has been trained, and (ii) updating the map information to the predicted map information; and updating information that includes (i) calculating updated map information based on the map information updated in (d), by using a gradient method, and (ii) updating the map information to the updated map information.

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.