Monocular Vision as a Range Sensor

One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robot’s field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360º view of the surrounding environment as part of map construction

An efficient object recognition and self-localization system for humanoid soccer robot

[[abstract]]In the RoboCup soccer humanoid league competition, the vision system is used to collect various environment information as the terminal data to finish the functions of object recognition, coordinate establishment, robot localization, robot tactic, barrier avoiding, etc. Thus, a real-time object recognition and high accurate self-localization system of the soccer robot becomes the key technology to improve the performance. In this work we proposed an efficient object recognition and self-localization system for the RoboCup soccer humanoid league rules of the 2009 competition. We proposed two methods : 1) In the object recognition part, the real-time vision-based method is based on the adaptive resolution method (ARM). It can select the most proper resolution for different situations in the competition. ARM can reduce the noises interference and make the object recognition system more robust as well. 2) In the self-localization part, we proposed a new approach, adaptive vision-based self-localization system (AVBSLS), which uses the trigonometric function to find the coarse location of the robot and further adopts the measuring artificial neural network technique to adjust the humanoid robot position adaptively. The experimental results indicate that the proposed system is not easily affected by the light illumination. The object recognition accuracy rate is more than 93% on average and the average frame rate can reach 32 fps (frame per second). It does not only maintain the higher recognition accuracy rate for the high resolution frames, but also increase the average frame rate for about 11 fps compared to the conventional high resolution approach and the average accuracy ratio of the localization is 92.3%.[[notice]]需補會議地點、主辦單位[[conferencetype]]國際[[conferencedate]]20100818~2010082

Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot

The vision system in soccer robot is needed to recognize the object around the robot environment. Omnidirectional vision system has been widely developed to find the object such as a ball, goalpost, and the white line in a field and recognized the distance and an angle between the object and robot. The most challenging in develop Omni-vision system is image distortion resulting from spherical mirror or lenses. This paper presents an efficient Omni-vision system using spherical lenses for real-time object detection. Aiming to overcome the image distortion and computation complexity, the distance calculation between object and robot from the spherical image is modeled using the neural network with optimized by particle swarm optimization. The experimental result shows the effectiveness of our development in the term of accuracy and processing time