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AIBO monocular depth perception through optical flow. Overview DECIS (Delft Cooperation on Intelligent Systems) has joined forces with several Dutch universities to investigate collaborative robot behavior. Together a total of 14 AIBO robot dogs have been purchased for research and to play in the RoboCup soccer competition. The AIBO is a versatile platform that helps explore new directions. A major challenge is the perception of the environment via robot vision. An interesting and promising direction in robot vision is depth perception through real time optical flow calculations. This technology will allow the AIBO to extract depth information from the scene viewed by its (single) onboard camera. The depth information can be used directly for getting a 3D sense of the environment useful for navigation, obstacle detection, etc. The aim is to implement existing optical flow algorithms in the AIBO software and explore new possibilities on this real-time robot platform. A possible real life test case is the Dutch AIBO soccer team; can AIBO depth perception lead to better performance in the soccer field? Introduction Optical flow Depth perception can be achieved through a number of optical cues including optical flow. Essentially information about an object’s position can be discovered by slightly changing the viewing position. The images from the different viewpoints are compared and using an algorithm depth information about the viewed scene can be calculated. Monocular depth perception through optical flow is the application of this process continuously while traveling through a space. Nearer points in the space will flow by quicker than far away points. To visualize this, imagine looking out the side window while driving on a highway; nearby trees move by fast while far away trees move by rather slowly. This reveals their relative depth with respect to the viewer. Their absolute distance to the viewer can be calculated if the viewer motion is known precisely. The interesting property of this technique is that depth information about the viewed scene is generated with a single camera simply moving through it. Examples of optical flow Below is an image from a car driving along a road. This image is one of a sequence from which the optical flow is calculated. The dots indicate the points that are being compared to the previous image and the lines indicate the corresponding vector of optical flow in those points. Note that the nearby points have a greater amount of flow (thus larger vector) than far away points. In this scene it is clear that optical flow can be used to get a sense of self-motion and at the same time see depth in the scene. For example the trees to the left are nearby in contrast to the open space on the right except for the small bush 1 generating some optical flow. If an object were on the road nearby it would be detected because it would generate relatively much optical flow, like the white stripe on the middle of the road. (source: http://www.ail.cs.gunma-u.ac.jp/~ohta/3-D.html) In the above example the scene is static; the only moving object is the observer/camera traveling through the environment. The reversed situation is also possible; in the example below the moving cars can be detected by a static camera because of the optic flow they generate in the otherwise static scene. This is another application of the same principle. More problematic would be scenes where the observer is moving in a dynamic environment because the optical flow generated by moving objects will interfere with the optical flow generated by the moving observer. (source : http://woodworm.cs.uml.edu/~rprice/ep/kuehne/) Project description Goal The goal of this research is to investigate whether real-time optical flow depth perception is a viable and useful enhancement for AIBO vision. Existing algorithms and techniques can be used to obtain depth information in the moving scene. The research challenge is 2 the implementation of the algorithms in the AIBO and testing the capabilities in an application domain. Project phases The project phases depend on the difficulties encountered and the progress made. The first phases concern the implementation and the last phase deals with using the depth perception for an application. It should be expected that there is a fair amount of overlap between the phases and they are intended only to give an overview. The first phase of the project consists of an assessment of the AIBO system in combination with optical flow processing. For example, the algorithms can be run on the AIBO or on a PC connected to the AIBO over wireless LAN. The choice will depend on required and available processing power, required image quality and frame rate, and the latency of the network. The second phase of the project addresses the problems associated with getting the optical flow algorithms to output decent depth information. These problems will be related to the AIBO platform, its camera and environment lighting conditions. If needed a combination might have to be made with for example the AIBO range finder to get more precise depth information when needed. One can also imagine a depth perceptioncalibration of vision using the range finder. The third phase of the project will focus on using the depth perception to carry out a task in the environment. There are a number of interesting possibilities open for the student to choose from. Some examples are: - 3D mapping of the environment for navigation purposes. - Object / obstacle detection and avoidance - Judging if small obstacles can be climbed over and implementing climbing routines (ROBORescue application) - When the project progresses quickly collaboration with the Dutch AIBO soccer team is possible. This is described below. AIBO soccer Dutch AIBO team Working close with the Dutch AIBO team is advised. There is a lot of AIBO expertise in this group and perception in a soccer game is a real-life challenge. Depth perception in the soccer field could give the Dutch AIBO team the advantage it needs to win the RoboCup 2006 competition. It could mean an advantage in the following areas: - An AIBO needs to locate itself on the field. More precise, quicker establishment of its own position 3 - could mean better reaction times and higher score rate. In a known environment, like the Robocup AIBO soccer field, an attempt can be made to detect moving objects in the environment based on optical flow. Optical flow might allow an AIBO to better perceive the motion of the ball, increasing the chances of interception and improving its aim. Associated research - - At the faculty of aerospace engineering at the TUDelft work is being done on developing optical flow depth perception techniques for Unmanned Aerial Vehicles (UAVs) vision. This group is also interested in the AIBO vision and collaborates with DECIS on UAV research. Dutch Aibo Team, http://www.dutchaiboteam.nl/ Student qualities The student should have affinity with problem solving, programming, computer vision, and robotics. The student should be willing to collaborate with other researchers in Utrecht, Delft and other cities on AIBO research. Contacts Graduation professor: Henk Corporaal Technische Universiteit Eindhoven [email protected] Mentor: Hamed Fatemi Technische Universiteit Eindhoven [email protected] Project Coordinator: Matthijs Amelink DECIS lab [email protected] 4