This papa describes a stereoscopic imaging algorithm that is modified for obstacledetection in low flying Unmanned Aerial Vehicles (UAVs). In this type of flight, obstacledetection must be carried out quickly for th...
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ISBN:
(纸本)9781424415403
This papa describes a stereoscopic imaging algorithm that is modified for obstacledetection in low flying Unmanned Aerial Vehicles (UAVs). In this type of flight, obstacledetection must be carried out quickly for the system to be effective in real time. Additionally, since the aircraft is dose to the ground, the horizon is usually at the top of the field of view and obstacles must be distinguished from the clutter of the terrain. The sparse edge detection and reconstruction algorithm proposed, produces fast but partial reconstructions of the environment. One image is passed through a series of edge detectors to generate a very sparse outline of the environment This outline is then correlated to the second image and the resulting reconstruction is added to a model of the environment Although each individual reconstruction is incomplete, the overall result after a short initialization period is a model of the environment that is more comprehensive than a single stereoscopic correlation run with a more detailed edge detector. Simulation of the algorithm on test image patterns showed an increase in performance relative to the length of the sequence of stereo pairs. On averaged the signal to noise ratio (SNR) for sparse edge reconstruction was significantly higher than that for single correlation with more detailed edge detectors. Additionally it was found that the processing speed of the sparse edge detection algorithm on a pair of stereoscopic images is faster than the processing carried out by a more detailed edge detector. A test flight was also carried out to test the algorithm in a more realistic scenario. The test confirmed that the sparse edge reconstruction algorithm resulted in a much more detailed view of the environment than if a single, more detailed edge detector had been used
This paper describes a stereoscopic imaging algorithm that is modified for obstacledetection in low flying Unmanned Aerial Vehicles (UAVs). In this type of flight, obstacledetection must be carried out quickly for t...
详细信息
This paper describes a stereoscopic imaging algorithm that is modified for obstacledetection in low flying Unmanned Aerial Vehicles (UAVs). In this type of flight, obstacledetection must be carried out quickly for the system to be effective in real time. Additionally, since the aircraft is close to the ground, the horizon is usually at the top of the field of view and obstacles must be distinguished from the clutter of the terrain. The sparse edge detection and reconstruction algorithm proposed, produces fast but partial reconstructions of the environment One image is passed through a series of edge detectors to generate a very sparse outline of the environment. This outline is then correlated to the second image and the resulting reconstruction is added to a model of the environment Although each individual reconstruction is incomplete, the overall result after a short initialization period is a model of the environment that is more comprehensive than a single stereoscopic correlation run with a more detailed edge detector. Simulation of the algorithm on test image patterns showed an increase in performance relative to the length of the sequence of stereo pairs. On average, the signal to noise ratio (SNR) for sparse edge reconstruction was significantly higher than that for single correlation with more detailed edge detectors. Additionally it was found that the processing speed of the sparse edge detection algorithm on a pair of stereoscopic images is faster than the processing carried out by a more detailed edge detector. A test flight was also carried out to test the algorithm in a more realistic scenario. The test confirmed that the sparse edge reconstruction algorithm resulted in a much more detailed view of the environment than if a single, more detailed edge detector had been used.
In this paper, a reflective force generation method of a force feedback joystick is suggested for a nonautonomous mobile robot maneuvered by a teleoperator. Three types of the reflective forces are defined such as obs...
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ISBN:
(纸本)0780382323
In this paper, a reflective force generation method of a force feedback joystick is suggested for a nonautonomous mobile robot maneuvered by a teleoperator. Three types of the reflective forces are defined such as obstacledetection, obstacleavoidance and robot posture recognition forces. To generate the reflective force for obstacle detection and avoidance, range data from ultrasonic sensors of the robot are used. Using these data, wall and center following of the robot are implemented by a fuzzy logic controller for obstacleavoidance. Next, the reflective force for robot posture recognition is generated using a gyro sensor of the robot. Thus the operator can not only maneuver the robot without collisions but also recognize the existence of obstacles and the posture of the robot by the reflective forces while driving the robot. Then, to determine the type of the reflective forces according to robot and obstacle states a state transition diagram is suggested. Finally, the experimental and simulation results with the mobile robot, ROBHAZ-DT developed by KIST show the effectiveness of the suggested method.
An effective approach to obstacle detection and avoidance for autonomous land vehicle (ALV) navigation in outdoor road environments using computer vision and image sequence techniques is proposed. To judge whether an ...
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An effective approach to obstacle detection and avoidance for autonomous land vehicle (ALV) navigation in outdoor road environments using computer vision and image sequence techniques is proposed. To judge whether an object newly appearing in the image of the current cycle taken by the ALV is an obstacle, the object shape boundary is first extracted from the image. After the translation from the ALV location in the current cycle to that in the next cycle is estimated, the position of the object shape in the image of the next cycle is predicted, using coordinate transformation techniques based on the assumption that the height of the object is zero. The predicted object shape is then matched with the extracted shape of the object in the image of the next cycle to decide whether the object is an obstacle. We use a reasonable distance measure to compute the correlation measure between two shapes for shape matching. Finally, a safe navigation point is determined, and a turn angle is computed to guide the ALV toward the navigation point for obstacleavoidance. Successful navigation tests show that the proposed approach is effective for obstacle detection and avoidance in outdoor road environments. (C) 2000 Elsevier Science B.V. All rights reserved.
A sensing system using sonar ring for mobile robot capable of rapid detection and avoidance of obstacles has been developed. In the system, a method of firing all ultrasonic sensors. on a sonar ring simultaneously is ...
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ISBN:
(纸本)0780372727
A sensing system using sonar ring for mobile robot capable of rapid detection and avoidance of obstacles has been developed. In the system, a method of firing all ultrasonic sensors. on a sonar ring simultaneously is used. And to eliminate error data due to crosstalks in the simultaneous firing, a filtering method based on pattern matching using neural network is used. To show the advantage of this method, a prototype of sensing system with 24 sonar sensors is fabricated. By installing the system to mobile robot, a scanning rate up to 50Hz of panoramic detection is achieved. The experimental results showed that a rapid navigation of mobile robot in a real environment becomes possible. This paper describes the basic concept, the method for sensing and avoidance of obstacles, configuration of the developed sensor system and experimental results of obstacle detection and avoidance.
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