Abstract: Identifying interfering features in the detection of an environment adjacent to a mobile platform, and associated systems and methods are disclosed herein. A representative method can include identifying candidate regions from a color image obtained by a color vision sensor carried by a mobile platform, filtering out a first region subset of regions based, at least in part, on non-image data obtained by a second sensor carried by the mobile platform, determining a second subset of regions as corresponding to an interfering feature based, at least in part, on color information, and performing environment detection based, at least in part, on the second subset of regions. Furthermore, the method can comprise transforming data corresponding to the subset of regions to integrate with non-color environment data obtained from another sensor carried by the mobile platform.

Abstract: A method includes generating one or more calibration instructions for calibrating one or more sensors coupled to a movable object and providing at least some of the one or more calibration instructions to a user interface. The at least some of the one or more calibration instructions includes human-readable user instructions for moving the movable object along a predetermined pattern. The method further includes calibrating one or more sensor parameters for the one or more sensors based at least in part on sensor data collected while the movable object moves along the predetermined pattern.

Abstract: A method includes generating one or more calibration instructions for calibrating one or more sensors coupled to a movable object and providing at least some of the one or more calibration instructions to a user interface. The at least some of the one or more calibration instructions includes human-readable user instructions for moving the movable object along a predetermined pattern. The method further includes calibrating one or more sensor parameters for the one or more sensors based at least in part on sensor data collected while the movable object moves along the predetermined pattern.

Abstract: A vehicle detection method includes obtaining a target image and depth information of each pixel in the target image, obtaining a distance value of a vehicle candidate area in the target image according to the target image and the depth information, and determining a detection model corresponding to the vehicle candidate area according to the distance value of the vehicle candidate area.

Abstract: A vehicle detection method includes obtaining a target image and depth information of each pixel in the target image, obtaining a distance value of a vehicle candidate area in the target image according to the target image and the depth information, and determining a detection model corresponding to the vehicle candidate area according to the distance value of the vehicle candidate area.

Abstract: Identifying interfering features in the detection of an environment adjacent to a mobile platform, and associated systems and methods are disclosed herein. A representative method can include identifying candidate regions from a color image obtained by a color vision sensor carried by a mobile platform, filtering out a first region subset of regions based, at least in part, on non-image data obtained by a second sensor carried by the mobile platform, determining a second subset of regions as corresponding to an interfering feature based, at least in part, on color information, and performing environment detection based, at least in part, on the second subset of regions. Furthermore, the method can comprise transforming data corresponding to the subset of regions to integrate with non-color environment data obtained from another sensor carried by the mobile platform.

Abstract: A computer-implemented method includes detecting a change in a spatial configuration of a plurality of sensors coupled to an unmanned aerial vehicle (UAV) based on a first set of sensor data from a plurality of sensors, generating one or more calibration instructions to cause the UAV to move along a predetermined pattern in response to detecting the change in the spatial configuration, and determining a subsequent spatial configuration of the plurality of sensors based at least in part on a second set of sensor data from the plurality of sensors. The second set of sensor data is collected by the plurality of sensors when the UAV is moving along the predetermined pattern.

Abstract: The present disclosure discloses methods and apparatuses for wave recognition, and unmanned aerial vehicles. The method includes: extracting a first image acquired by an image acquisition apparatus at a first moment and a second image acquired by the image acquisition apparatus at a second moment; extracting a target region in each of the first image and the second image; comparing feature information of the target region in the first image with feature information of the target region in the second image; and determining whether the target region is a wave according to a result of the comparing of the feature information. The methods and apparatuses for wave recognition, and unmanned aerial vehicles recognize a wave in an image based on the change of feature information of a target region in the image at different times.

Abstract: A method of calibrating an imaging system may includes capturing images using at least one imaging device, identifying feature points in the images, identifying calibration points from among the feature points, and determining a posture of the at least one imaging device or a different imaging device based on positions of the calibration points in the images.

Abstract: A method for processing image information in an image to determine a movement for a movable object. The method includes detecting whether the image includes a water surface or a sky based on the image information in the image, And, in response to detecting that the image includes the water surface or the sky, determining a technique from a plurality of techniques for calculating a depth map, generating the depth map using the technique, and determining a movement parameter for the movable object using the depth map.

Abstract: An exposure control method is provided for a control device. The method includes acquiring an output image from data outputted by a depth sensor according to a current exposure parameter, determining a target image of a target object in the output image, and determining a first exposure parameter according to the brightness of the target image. The first exposure parameter is used for controlling the next exposure of the depth sensor.

Abstract: A computer-implemented method includes detecting a change in a spatial configuration of a plurality of sensors coupled to an unmanned aerial vehicle (UAV) based on a first set of sensor data from a plurality of sensors, generating one or more calibration instructions to cause the UAV to move along a predetermined pattern in response to detecting the change in the spatial configuration, and determining a subsequent spatial configuration of the plurality of sensors based at least in part on a second set of sensor data from the plurality of sensors. The second set of sensor data is collected by the plurality of sensors when the UAV is moving along the predetermined pattern.