Active markers for outdoor and indoor robot localization

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or natural or artificial landmarks (Jang et al., 2002), (Betke and Gurvits, 1997). Many solutions adopt artificial landmarks (Batalin and Sukhatme, 2003); (Maeda et al., 2003); (Lamon et al., 2001); (Ma et al., 2002); (Parker et al., 2004); (Roh et al., 1997); (Fiala, 2004) because they can be designed for optimal detectability even under adverse environmental conditions. The system presented in this paper provides absolute localization from scratch to a robot in a dynamic environment, by means of a webcam and the use an optical active marker. It is quite obvious that absolute positioning systems offer great advantages over relative methods, because their readings can be immediately used. In many cases high precision is also not an issue, especially when the system is used for “driving the robot around”, as it happens for instance in surveillance robots. Furthermore, absolute systems do not suffer from “robot stealing” problems, since each reading is either independent from all the previous ones, or, if previous readings are used to quicken or to enhance the current one, a procedure exists that allows not to use them with little performance degradation. 3 Description of the system The localization system was designed to aid MORGUL, MObile Robot for Guarding University Laboratories, in its task of patrolling an area in the campus of the University of Brescia (Cassinis et al., 2005a), (Cassinis et al., 2005b). MORGUL (Figure 1) is a Pioneer 3AT mobile autonomous robot equipped with an on-board PC, which is connected to the local area network and to the Internet via several different channels, even if the most used one in indoor and short-range outdoor missions is a wi-fi communication link. Unlike most absolute localization systems, that use robot-mounted sensors to gather information from fixed landmarks, the solution we adopted reverses this philosophy, using fixed sensors that “look” at the robot. This solution may seem complicated and non-economical, but it should be kept in mind that modern networking systems (specially wireless ones) make it usually very easy to place internet-connected devices around the environment. In this approach robots are low-cost mobile devices, that only take on board actuators and sensors that are needed for a reliable accomplishment of their task, embedding the remaining portion of sensors and processing units into the environment, using whenever possible resources that are already available for other reasons. In the patrolling area, for instance, the used cameras can be the same used for a fixed surveillance system. This point of view in the recent years has led to the development of a message-oriented middleware called DCDT (Device Communities Development Toolkit) that implements a reliable and stable communication layer as required by this approach. Every device outside the robot can autonomously perform its task, offering its services only when and where required in this sort of distributed sensing and processing system, in which the DCDT takes responsibility of delivering messages throughout the system, managing each single active and independent software agent, called member. In our project, the robot’s localization is conceived as an autonomous task provided by a member of the DCDT system that interacts with the community of interconnected devices in a transparent way, regardless of the physical communication means used. Figure 1: MORGUL robot. The localization is performed by a visual-based system that recognizes an active marker when the robot gets unsure about its position. The visual system consists of a commercial off-the-shelf webcam, namely a Philips ToUCam Pro II PCVC840K with a resolution of 640x480 pixels, connected via USB link to a PC. As far as the active marker is concerned, several tests have demonstrated that in outdoor environments the best results can be obtained with a series of high brightness LEDs, while for indoor use infrared LEDs can be used with good results, even if the webcam is not very sensitive in the infrared region. The LED clusters are controlled by a microcontroller unit connected via a serial line to the robot PC that runs a suitable autonomous DCDT member. Cameras should be placed as high as possible in order to minimize errors due to geometrical distortions of the lenses: this translates in placing them close to the ceiling indoor, and at least at the height of the first floor when operating outdoor. This, by the way, is a normal placement for surveillance cameras. The system can be composed of several cameras, although, in a flat environment
where the height of the marker is constant, a single camera is sufficient to solve the localization problem. More cameras can be used to cover wide areas and, if their fields of view are overlapping, to check the operation of the system. This paper mainly deals with the marker detection problem, and does not go into the details of the localization algorithm. So far, we used a very simple method based on a grid of calibration points and on linear interpolation among these points. 4 Marker recognition algorithm While designing the marker and the recognition algorithm, we had to keep in mind several points, among which the most important ones are that in daylight, and particularly in full sunshine, the light emitted by any artificial device cannot compete with the natural light emitted by the sun, unless enormous amounts of energy are used. This means that the use of a time-variant marker is mandatory, and that its characteristics must be carefully chosen in order to avoid confusion with other direct or indirect light sources. A first attempt at using colour-varying markers was discarded, because this would require processing the image in at least two different colours. The idea of using a low frequency flashing light beacon was also abandoned, because it would make it more complex to adapt the marker brightness to ambient light. Finally, it was decided to use a marker that emits flashes at a fixed frequency, whose period is quite close to the camera frame acquisition time. Since the used camera, as almost all CCD webcams, has an electronic shutter during whose opening time light is integrated on the sensor, the idea was to have this shutter, that operate at constant frequency and at a fixed duty cycle, beat with the light emitted by the marker, that is also turned on and off at preset times. The result is that the pixels pertaining to the marker appear with a brightness that periodically oscillates following a quasi-triangular waveform whose frequency is the beat between the shutter and the marker frequency, and whose shape is only influenced by the ratio of the shutter and the marker’s duty cycle. The recognition algorithm consists of several steps that will be described separately in the sequel: acquisition, background elimination, pixel pattern recognition, blobs identification and finally marker detection. 4.1 Acquisition During this step images are acquired from the webcam in VGA resolution and saved in a suitable memory area. While in indoor environments we could rely on the marker being one of the brightest objects the camera can see, in outdoor operation this is unrealistic: an image differencing technique has to be used, and further processing takes place on differential images obtained subtracting each frame from the previous one. Figure 2 shows the marker, seen from a distance of about 26 metres, in a slightly cloudy day. It is easy to imagine that, in bright sunshine, it can be much more difficult to identify. Figure 2: Outdoor image - Marker ON (a) and OFF (b). 4.2 Background subtraction In image differencing, locations of change are indicated by large values in the difference image obtained subtracting the corresponding pixels of two consequent frames. Due to its simplicity, this method is widely applied in change detection algorithm used in object tracking, intruder surveillance systems, vehicle surveillance systems, and inter-frame data compression. Figure 3: Shutter - marker relationship. In our recognition algorithm, we make subtraction in the luminance channel of two consequent frames. A positive value in a pixel in the image difference represent the fact that a bright spot has appeared in an image, while a negative value indicates that it has disappeared. 4.3 Pixel pattern recognition In order to localize the robot, we must find the pixels whose behaviour matches the pattern expected from the marker, i.e. a periodical repetition of n frames where it appears to be ON and of m frames where it is OFF. In the difference images collected from the previous phase with the chosen timing this behaviour is represented by a positive value when the marker appears in an image, n-1 values close to 0 while it remains present, a negative value while it is disappearing and m-1 values close to 0 when it is in the OFF state. This sequence can be binarised against a given threshold, and it generates a sequence that, as it can be seen in Figure 3, is 11100001110000…, with n=3 and m=4. Recognizing this pattern could be simply done with a mask, but inaccuracies in the timing of the various components require a slightly more sophisticated approach.
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Page 5 and 6: The two sessions of experiments too
Page 7 and 8: References Max Blob Value Blob Mean

Active markers for outdoor and indoor robot localization

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