4 results about "Flood fill" patented technology

The invention discloses a control method and system for an unmanned aerial vehicle to pass through a complex obstacle based on visual perception. The method comprises the following steps: synchronously acquiring an RGB image and a depth image of an obstacle in front of the unmanned aerial vehicle; inputting the acquired RGB image into a pre-trained deep neural network model, and performing semantic segmentation on a cavity area or a mesh structure gap of an obstacle in the image to generate a corresponding binary mask image; removing the binaryzation masks which are discontinuous with the barrier masks in depth distribution, performing expansion operation on the residual binaryzation masks, and filling the background masks with flood; mask reverse selection is carried out, hole and gap masks of a non-obstacle area are extracted, and a maximum rectangular area larger than the forward section of the unmanned aerial vehicle is obtained based on the boundaries of the mask areas to serve as a passable area; and based on the position of the passable area, controlling the unmanned aerial vehicle to cross the obstacle. According to the invention, accurate identification, path planning and attitude control of the cavity area capable of being traversed in the obstacle can be realized.

This application discloses a robot continuation planning method based on obstacle expansion processing, including: Step A, when the robot scans for dangerous obstacles, it marks the area occupied by the dangerous obstacles, then expands the area occupied by the dangerous obstacles, and then the robot walks along the boundary of the expanded area of ​​the dangerous obstacles. At the same time, the path formed by the robot walking along the obstacle avoidance boundary of the expanded map is marked as an edge-closed path; Step B, the robot clears the expanded obstacle avoidance area corresponding to the dangerous obstacles to obtain a preprocessed map; then, a flood filling algorithm is performed on the preprocessed map; Step C, the robot clears the edge compensation path from the edge-closed path to obtain an edge-effective path; Step D, the target navigation point is calculated by combining the passable area filled in the preprocessed map and the edge-effective path; Step E, the robot is guided to the target navigation point by judging the area where the target navigation point is located and the reachability of the target navigation point.

This application discloses a target navigation point planning method, chip, and robot based on dangerous obstacles. The target navigation point planning method includes: Step A, the robot scans dangerous obstacles and marks the areas occupied by dangerous obstacles, then expands the areas occupied by dangerous obstacles to obtain expanded dangerous obstacle areas and corresponding expanded obstacle avoidance areas, and then walks along the boundary of the expanded dangerous obstacle areas, while marking the path formed by the robot walking along the obstacle boundary as an edge-closed path; Step B, the corresponding expanded obstacle avoidance areas are cleared to obtain a preprocessed map; then a flood filling algorithm is performed on the preprocessed map to fill in a passable area for the robot to enter the non-working area; Step C, the robot clears the edge-compensated path from the edge-closed path to obtain an edge-effective path; Step D, the target navigation point is calculated by combining the passable area filled in the preprocessed map and the edge-effective path described in Step C.

The invention relates to a water surface target detection method based on an improved flood filling algorithm, belongs to the technical field of target detection, and solves the problems that an existing method based on fixed threshold segmentation is poor in adaptability to illumination changes, and a traditional background modeling method cannot effectively separate a background due to continuous existence of a dynamic target, and is poor in detection accuracy. And the water surface target detection precision is not enough. Comprising the following steps: collecting and pre-processing video stream data of a water surface area under different time periods and different illumination conditions to generate an empty scene static background image; performing water surface region segmentation processing on the empty scene static background image by using an improved flood filling algorithm to generate a water surface region mask; and performing water surface target detection based on video stream data acquired in real time and the water surface area mask to obtain a water surface target detection result. And accurate detection of a water surface target is realized.