Nessie II Autonomous Underwater Vehicle - Xsens

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6technique were found as well.The noise estimation of the sonar’s image can be defined asthe normalized absolute difference between the sonar’s imageand its model. The model can be defined as the ideal sonarimage which we can get from the ideal sonar working in theideal conditions.Thus, if we have the image called SonarPic and its modelwhich is called FirstModel then we can estimate the totalnoise value with the equation:Total Noise= ∑∑xySonarPic(x,y)− FirstModel(x,y)where x ∈[0,MaxWidth[,y ∈[0,MaxHeight[Clearly, the model has the same size as the sonar’s image.From the total noise equation we can calculate the maximumnoise value which we can get. The maximum noise value isthe noise value between the model and its inverse version; in 8bit images the maximum noise value can be calculated asfollowing (given that the model is back or white):Max Noise = 255×MaxWidth×MaxHeightFinally, the normalized noise estimation in the range of 0 to10 can be calculated as following:Total Noise×10Noise =Max NoiseCalculating the noise estimation in the sonar’s imagerequires the model image. This model can be obtained fromthe ideal sonar working in the ideal conditions. Unfortunately,ideal sonar doesn’t exist in reality. However we can build themodel manually. In other words, we will draw the modelcorresponding to a particular position of the sonar. Buildingthe model manually is one of the disadvantages of this methodand drawing the model can be one kind of a black art. On theother hand, this method gives us the opportunity to fairlycompare the images (obtained using different gain values)rather than comparing between them using the naked eye. Thiscomparison between images can be fair enough since they aremeasured using the same measuring tool which is the model inour case.Noise estimation in the sonar’s image can be used in threedifferent arenas:• To compare between the impacts of the differentFilters on the same sonar’s image.• To compare between the impacts of differentparameters of the same image enhancementtechnique, this can be different threshold values,different structuring elements, different filterkernels, …etc• To compare between sonar images obtained in thesame position with different Gain values.Figure 15 shows one of the sonar images its model. Thenoise in the image is: 1.81616.Figure 15: A sonar image on the left with thecorresponding tank boundaries on the right.All image processing applications include one stage forimage enhancement. This year the best chain of procedures toenhance the image will be applied. This can be done byestimating the noise after each procedure and compare thenoise value with the previous estimated value.After applying one of the image enhancements we canestimate the improvements happened in the image by theequation:EE = Noise after− Noise beforeNoiseaftreisWhere EE stands for enhancement estimation,the noise estimation after applying the image enhancementtechnique. Noise is the estimated noise before applyingbeforeit. The previous equation shows that EE ranges between -10and 10 depending on the noise definition in this report. Thefollowing table illustrates the program output after applyingdifferent image enhancement techniques. The noise valueincreased after applying the closing, this is because closingincreases the overall brightness of the image. Figure 16shows the closed image and the same image filtered with acanny filter (the source image is shown in Figure 15).Original sonar noise 1.81616Noise after closing 2.00854Noise after Canny filter 0.00192515Figure 16: A sonar image on the left and the same imageon the right with noise filtered out.After performing Hough Transform and retrieving the
7brightest line in the image and the closest to sonar, analgorithm should search for the brightest perpendicular line toit. The intersection of these two lines will be the corner of thepool which we are searching for. Figure 17 shows thedetected lines in the original image (Figure 15).through the equation:real dis =max depth222( measured dis)− ( − depth)However, the mid water target is not in the depth (maxdepth/2). Nevertheless, this calculation will decrease the errorwhile estimating the real distance with the measured one.Figure 19 shows the estimated value in the case where the midwater target is below the max depth/2 level. The estimateddistance value is closer to the real distance value than themeasured one.Figure 17: A sonar image showing the detection of the poolcorner.Image enhancements to detect the mid water target are bedone by applying a canny filter in the pool area, and searchingfor the contours inside it. Increasing the result confidence willbe sustained through two stages, the sonar focusing stage andthe correction stage.Sonar focusing can be done after the first scanning of theenvironment with 360° and range equal to the length of thevirtual pool (length of the competition area). In the first scanwe can estimate the position of the mid water target. The nextscan can be done with a range bigger than the estimateddistance slightly, and the scanning sector can be 30° whichincludes the target. This procedure can enhance the resolutionof the image since the range will be smaller than the firstrange and the noise will be less than the noise in the firstscanned image.After detecting the mid water target and measuring thedistance between the target and the sonar, a correctionparameter should be applied on this distance. The SLAMModule is expecting the mid water coordinates (retrieved fromsonar) to be in the xy plane. Unfortunately, this is not the case.Figure 18 shows the measured distance and the real distancein the xy plane.Figure 19: Real, measured, and the estimated distancebetween the target and the sonar.D. Integrated SINS and SLAM1) General SLAMIn this section, the concept and the research evolution ofSimultaneous Localisation and Mapping (SLAM) (SLAM isreferred as Concurrent Mapping and Localisation (CML) insome of the literature) will be introduced in detail. Figure 20shows the structure of SLAM. The first consistent solution ofSLAM came from Randall Smith, Matthew Self and PeterChesseman [1]. Following that book, SLAM research hasbecome more and more popular in the autonomous vehiclearea because self localisation is a critical question to anautonomous vehicle. An autonomous system must answerthree questions: “Where am I?” “Where am I going?” and“How do I get there?” The first one can be thought as the mostimportant one of these three questions [2].Figure 18: The real and the measured distance betweenthe target and the sonar.To decrease the error in the distance value, we canapproximate the mid water target depth to be fixed in (MaxDepth/2), since we know the sonar’s depth (from the pressuresensor) we can estimate the real distance more accuratelyFigure 20: The structure of the SLAM problem.Randall Smith presented a general theory for estimating the
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Nessie II Autonomous Underwater Vehicle - Xsens

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