Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy. A feature amount obtained by a point cloud that is a dense detection point of LiDAR or the like and a feature amount of a sparse detection point of a millimeter wave radar or the like within a predetermined distance are grouped and integrated with reference to the position of the feature amount of the dense detection point, the point cloud is restored using an integrated feature amount of the dense detection point, and object recognition processing such as semantic segmentation is performed. The present disclosure can be applied to a mobile body.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy. A feature amount calculation unit of a user's car calculates a feature amount for an object recognition result in stages in each of a plurality of hierarchies, an acquisition unit acquires a feature amount for an object recognition result calculated by a feature amount calculation unit of another car, and a recognition unit performs the object recognition on the basis of the feature amount calculated by the user's car and the acquired feature amount calculated by the another car. The present disclosure can be applied to a mobile body.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy. A feature amount calculation unit of a user's car calculates a feature amount for an object recognition result in stages in each of a plurality of hierarchies, an acquisition unit acquires a feature amount for an object recognition result calculated by a feature amount calculation unit of another car, and a recognition unit performs the object recognition on the basis of the feature amount calculated by the user's car and the acquired feature amount calculated by the another car. The present disclosure can be applied to a mobile body.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy. A feature amount obtained by a point cloud that is a dense detection point of LiDAR or the like and a feature amount of a sparse detection point of a millimeter wave radar or the like within a predetermined distance are grouped and integrated with reference to the position of the feature amount of the dense detection point, the point cloud is restored using an integrated feature amount of the dense detection point, and object recognition processing such as semantic segmentation is performed. The present disclosure can be applied to a mobile body.

Abstract: A multi-operation unit integration device having scale expandability and includes a plurality of operation units each of which includes a movable unit; and an integration module. The integration module includes an operation timing unit that gives operation timings of the plurality of operation units based on an operation instruction input from an outside, and the operation unit includes: a plurality of operation learning units that generate a control signal given to the movable unit according to an operation timing signal from the operation timing unit of the integration module; drive means for driving the movable unit of the operation unit according to the control signal; and a sensor that detects a state quantity of the movable unit driven by the drive means. An autonomous learning type robot device is configured using the multi-operation unit integration device as a control portion.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy. A feature amount calculation unit of a user's car calculates a feature amount for an object recognition result in stages in each of a plurality of hierarchies, an acquisition unit acquires a feature amount for an object recognition result calculated by a feature amount calculation unit of another car, and a recognition unit performs the object recognition on the basis of the feature amount calculated by the user's car and the acquired feature amount calculated by the another car. The present disclosure can be applied to a mobile body.

Abstract: An object is to provide a database construction system for machine-learning that can automatically simply create virtual image data and teacher data in large volumes. A database construction system for machine-learning includes: a three-dimensional shape data input unit configured to input three-dimensional shape information about a topographic feature or a building acquired at three-dimensional shape information measuring means; a three-dimensional simulator unit configured to automatically recognize and sort environment information from the three-dimensional shape information; and a teacher data output unit configured to output virtual sensor data and teacher data based on the environment information recognized at the three-dimensional simulator unit and a sensor parameter of a sensor.

Abstract: Provided is an environment map automatic creation device using a flying object. The environment map automatic creation device includes a travelable area extraction unit that extracts a travelable area where a vehicle can travel in a certain area based on three-dimensional shape information of an area for creating an environmental map, which is acquired by a sensor in a flying object, an area category determination unit that determines a category of the travelable area, a complementary portion determination unit that determines whether or not to measure a complementary portion which complements the travelable area based on the travelable area and the category, and a complementary measurement portion presentation unit that presents the complementary measurement portion.

Abstract: When a combination of a plurality of sensors is used for obstacle detection, the present invention is to detect the relative positions of the sensors, to correct inter-sensor parameters, and to provide accurate obstacle detection. This calibration system is provided with a first landmark detecting unit for detecting the position of a first landmark from three-dimensional shape information; a landmark associating unit for determining a correspondence relation, between the first landmark position detected by the first landmark detecting unit and an attachment position to the vehicle of the first landmark estimated by a vehicle landmark relative position estimating unit; and a vehicle sensor relative orientation estimating unit for estimating the relative position and orientation of the vehicle and a first measurement section based on the information from the vehicle landmark relative position estimating unit and a landmark associating unit.

Abstract: An object is to provide a database construction system for machine-learning that can automatically simply create virtual image data and teacher data in large volumes. A database construction system for machine-learning includes: a three-dimensional shape data input unit configured to input three-dimensional shape information about a topographic feature or a building acquired at three-dimensional shape information measuring means; a three-dimensional simulator unit configured to automatically recognize and sort environment information from the three-dimensional shape information; and a teacher data output unit configured to output virtual sensor data and teacher data based on the environment information recognized at the three-dimensional simulator unit and a sensor parameter of a sensor.

Abstract: Provided is an environment map automatic creation device using a flying object. The environment map automatic creation device includes a travelable area extraction unit that extracts a travelable area where a vehicle can travel in a certain area based on three-dimensional shape information of an area for creating an environmental map, which is acquired by a sensor in a flying object, an area category determination unit that determines a category of the travelable area, a complementary portion determination unit that determines whether or not to measure a complementary portion which complements the travelable area based on the travelable area and the category, and a complementary measurement portion presentation unit that presents the complementary measurement portion.

Abstract: A multi-operation unit integration device having scale expandability and includes a plurality of operation units each of which includes a movable unit; and an integration module. The integration module includes an operation timing unit that gives operation timings of the plurality of operation units based on an operation instruction input from an outside, and the operation unit includes: a plurality of operation learning units that generate a control signal given to the movable unit according to an operation timing signal from the operation timing unit of the integration module; drive means for driving the movable unit of the operation unit according to the control signal; and a sensor that detects a state quantity of the movable unit driven by the drive means. An autonomous learning type robot device is configured using the multi-operation unit integration device as a control portion.

Abstract: When a combination of a plurality of sensors is used for obstacle detection, the present invention is to detect the relative positions of the sensors, to correct inter-sensor parameters, and to provide accurate obstacle detection. This calibration system is provided with a first landmark detecting unit for detecting the position of a first landmark from three-dimensional shape information; a landmark associating unit for determining a correspondence relation, between the first landmark position detected by the first landmark detecting unit and an attachment position to the vehicle of the first landmark estimated by a vehicle landmark relative position estimating unit; and a vehicle sensor relative orientation estimating unit for estimating the relative position and orientation of the vehicle and a first measurement section based on the information from the vehicle landmark relative position estimating unit and a landmark associating unit.