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Figure 11: (a) Image display processing. (b) Bidimensional map of the environment. [24] sonar distance data with stereo disparity; disparity is calculated along a horizontal line taken at a chosen height in the stereo images. Main points: • Using color as an immediate and efficient mean to convey infor- mation • Using geometric overlays to enhance distance estimation • Sensor fusion balances weaknesses of single sensors 21
3.2 Fusion of laser and visual data for robot motion planning and collision avoidance The paper of Baltzakis et al. [54] proposes a SLAM (Simultaneous Localization and Mapping) algorithm based on fusion of 2D laser range data and stereo visual data. The proposed method uses stereo disparity to correct laser measurements where they are evidently wrong. The algorithm initially creates a 3D model of the environment as a series of vertical walls based on the 2D laser scan. This model forcibly omits all the objects which do not intersect the plane of the laser scan, since they cannot be detected by the laser sensor. Then, the pixels of one of the stereo images are ray-traced to the 3D model, and the 3D coordinates correspondent to each pixel are ob- tained. Finally, the algorithm re-projects each pixel onto the second image. If the attributes (color, intensity, etc.) of the pixel in the second image are similar to the attributes of the correspondent one in the first image, then the value measured by the laser for that pixel is assumed to be correct. Instead, if the attributes of the pixels are different, the pixels are assumed to belong to an object which is nearer or further than the distance measured by the laser. In this case, a distance estimation, based on the disparity between the images, is performed. Range esti- mates are accumulated on a 2D occupancy grid (in order to decrease the inaccuracy deriving by image noise or lack of texture). A simple collision avoidance algorithm, using the mapping method just exposed, is presented. The algorithm is tested both in artificial and in real environments, showing to have good results in aiding navigation. Main points: • 3D map of the environment based on range sensors and video • Using stereo to correct and integrate data from range sensors 22
Page 1 and 2: UNIVERSITÀ DEGLI STUDI DI CATANIA
Page 3 and 4: 4.5 Summary and analysis . . . . .
Page 5 and 6: ing telerobotic systems continues t
Page 7 and 8: tion. Section 3 describes the state
Page 9 and 10: • to be registered in 3D (that is
Page 11 and 12: lay in advance. In video-based AR s
Page 13 and 14: The point R at the intersection of
Page 15 and 16: 2.3 Stereoscopic visualization A st
Page 17 and 18: disparity and will be seen as if th
Page 19 and 20: Figure 10: (a) CristalEyes shutter
Page 21: 3.1 A sensor fusion based user inte
Page 25 and 26: the edges of the graph. Once the ro
Page 27 and 28: Figure 13: Modified INEEL interface
Page 29 and 30: enefit from having both video and m
Page 31 and 32: mesh of the environment onto the le
Page 33 and 34: of monocular depth cues. Besides, w
Page 35 and 36: 4.1 The 3MORDUC platform The 3MORDU
Page 37 and 38: Figure 18: The STH-MDCS2-VAR-C ster
Page 39 and 40: higher mean speeds. Main points:
Page 41 and 42: tion permits a significant decrease
Page 43 and 44: Figure 20: (a) Proximity planes ove
Page 45 and 46: 5 Proposed method: AR stereoscopic
Page 47 and 48: 5.2 Research development strategy T
Page 49 and 50: Several approaches that permit an a
Page 51 and 52: the left and the right image. Real
Page 53 and 54: 6 Effective multi-sensor visual rep
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Page 57 and 58: of [25] is sensitive to calibration
Page 59 and 60: Figure 25: (a) Visual artifacts cre
Page 61 and 62: 7 Laser-camera alignment and calibr
Page 63 and 64: Figure 26: Graphical representation
Page 65 and 66: Figure 27: (a) Overlay at the begin
Page 67 and 68: parameters found the calibration pr
Page 69 and 70: to the maximum value (white). Inten
Page 71 and 72: variate the single parameters while
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Figure 31: Unprojection of the edge
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Figure 32: (a) Alignment using feed
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Since the last case is rather unlik
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(see the box on the left of figure
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elonging to the same model may have
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could contain artifacts which the o
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cially useful to eliminate vertical
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10 Conclusions This work has presen
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References [1] B. Davies. A review
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B. MacIntyre. Recent advances in au
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[29] M. Ferre, R. Aracil, and MA Sa
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[46] L. Lipton. Stereographics, Dev
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[64] OpenGL website. http://www.ope
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