This paper describes RUR53, the unmanned mobile manipulator robot developed by the Desert Lion team of the University of Padova (Italy), and its experience in Challenge 2 and the Grand Challenge of the first Mohamed Bin Zayed International Robotics Challenge (Abu Dhabi, March 2017). According to the competition requirements, the robot is able to freely navigate inside an outdoor arena; locate and reach a panel; recognize and manipulate a wrench; use this wrench to physically operate a valve stem on the panel itself. RUR53 is able to perform these tasks both autonomously and in teleoperation mode. The paper details the adopted hardware and software architectures, focusing on its key aspects: modularity, generality, and the ability of exploiting sensor feedback. These features let the team rank third in the Gran Challenge in collaboration with the Czech Technical University in Prague, Czech Republic, the University of Pennsylvania, USA, and the University of Lincoln, UK. Tests performed both in the Challenge arena and in the lab are presented and discussed, focusing on the strengths and limitations of the proposed wrench and valve classification and recognition algorithms. Lessons learned are also detailed.
Working outdoors means dealing with light variations. This is a known problem for outdoors applications and is even more crucial when detecting obstacles. In our case, besides the light variations, another issue has to be considered: the panel to be detected is black. It should be noticed that a surface that appears black does not reflect the visible light. As stated in @cite_29 , in order to detect the panel in such conditions, other techniques have to be adopted instead of exploiting vision data. For these reasons, we decided to exploit the laser range data (see ).
@cite_29
1704999350 2536722097 1816614560 1998270967
Illumination conditions cause problems for many computer vision algorithms. In particular, shadows in an image can cause segmentation, tracking, or recognition algorithms to fail. In this paper we propose a method to process a 3-band colour image to locate, and subsequently remove shadows. The result is a 3-band colour image which contains all the original salient information in the image, except that the shadows are gone.We use the method set out in [1] to derive a 1-d illumination invariant shadow-free image. We then use this invariant image together with the original image to locate shadow edges. By setting these shadow edges to zero in an edge representation of the original image, and by subsequently re-integrating this edge representation by a method paralleling lightness recovery, we are able to arrive at our sought after full colour, shadow free image. Preliminary results reported in the paper show that the method is effective.A caveat for the application of the method is that we must have a calibrated camera. We show in this paper that a good calibration can be achieved simply by recording a sequence of images of a fixed outdoor scene over the course of a day. After calibration, only a single image is required for shadow removal. It is shown that the resulting calibration is close to that achievable using measurements of the camera's sensitivity functions. One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach. Successfully detecting shadows in still images is challenging yet has wide applications. Shadow properties and features are very important for shadow detection and processing. The aim of this work is to find some new physical properties of shadows and use them as shadow features to design an effective shadow detection method for outdoor color images. We observe that although the spectral power distribution (SPD) of daylight and that of skylight are quite different, in each channel, the spectrum ratio of the point-wise product of daylight SPD with sRGB color matching functions (CMFs) to the point-wise product of skylight SPD with sRGB CMFs roughly approximates a constant. This further leads to that the ratios of linear sRGB pixel values of surfaces illuminated by daylight (in non-shadow regions) to those illuminated by skylight (in shadow regions) equal to a constant in each channel. Following this observation, we calculated the spectrum ratios under various Sun angles and further found out four new shadow properties. With these properties as shadow features, we developed a simple shadow detection method to quickly locate shadows in single still images. In our method, we classify an edge as a shadow or non-shadow edge by verifying whether the pixel values on both sides of the Canny edges satisfy the three shadow verification criteria derived from the shadow properties. Extensive experiments and comparison show that our method outperforms state-of-the-art shadow detection methods. HighlightsWe found the ratios of regions lit by daylight vs. by skylight equal to a constant.We calculated spectrum ratios and found out four new shadow properties.Following the new shadow properties, we proposed a simple shadow detection method.We conducted most extensive experiments and comparison.We also tested our method on images without shadows. Recently, a method for removing shadows from colour images was developed ( in IEEE Trans. Pattern Anal. Mach. Intell. 28:59---68, 2006) that relies upon finding a special direction in a 2D chromaticity feature space. This "invariant direction" is that for which particular colour features, when projected into 1D, produce a greyscale image which is approximately invariant to intensity and colour of scene illumination. Thus shadows, which are in essence a particular type of lighting, are greatly attenuated. The main approach to finding this special angle is a camera calibration: a colour target is imaged under many different lights, and the direction that best makes colour patch images equal across illuminants is the invariant direction. Here, we take a different approach. In this work, instead of a camera calibration we aim at finding the invariant direction from evidence in the colour image itself. Specifically, we recognize that producing a 1D projection in the correct invariant direction will result in a 1D distribution of pixel values that have smaller entropy than projecting in the wrong direction. The reason is that the correct projection results in a probability distribution spike, for pixels all the same except differing by the lighting that produced their observed RGB values and therefore lying along a line with orientation equal to the invariant direction. Hence we seek that projection which produces a type of intrinsic, independent of lighting reflectance-information only image by minimizing entropy, and from there go on to remove shadows as previously. To be able to develop an effective description of the entropy-minimization task, we go over to the quadratic entropy, rather than Shannon's definition. Replacing the observed pixels with a kernel density probability distribution, the quadratic entropy can be written as a very simple formulation, and can be evaluated using the efficient Fast Gauss Transform. The entropy, written in this embodiment, has the advantage that it is more insensitive to quantization than is the usual definition. The resulting algorithm is quite reliable, and the shadow removal step produces good shadow-free colour image results whenever strong shadow edges are present in the image. In most cases studied, entropy has a strong minimum for the invariant direction, revealing a new property of image formation.