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Object Position and Orientation Detection For Mars Science Laboratory Helicopter Test Imagery Michael Johnson EE 368 Stanford University June 5, 2012 Background • The Mars Science Laboratory Terminal Descent Sensor, the rover’s landing radar, underwent a field test campaign in the Mojave Desert. • One purpose of the field test was to gauge how the rover’s position during the sky crane descent phase might impact the return of the radar signal during this crucial phase of the mission. • To test this, the landing radar was affixed to a helicopter, and a mock up of the rover, with targets attached to the top deck, was attached to a wench underneath the helicopter. The radar was operated while the position of the rover mock up was varied. • A camera was positioned near the wench, facing down, to image the rover as the test was underway. Time-tagged images were captured at a rate of 6 frames per second, which will be used to estimate the position of the rover during the test and matched with specific times of radar test data. Problem Statement • As stated in the background, the problem is to derive estimations of an object’s position and orientation in an image. • In each image, the object (in this case the rover mock up) will be: – Translated along the x and y dimensions. – Scaled in size depending on the distance from the camera. – Rotated due to helicopter maneuvers, wind, and other factors. • Although the mockup may also experience roll and pitch effects, these are assumed to be minimal due to the nature of the test, and will be ignored. • Image quality can vary due to: – Angle of the sun causing bright reflections on the rover surfaces. – Shadows cast by the helicopter over the rover, obscuring the targets. Proposed Solution • It is proposed to detect the rover’s position and orientation in the images using the following three steps: – Perform Sobel edge detection to detect the edges of the circular targets. – Calculate the Circular Hough Transform on the filtered images to find the circular targets by detecting the peaks in the Circular Hough Transform. This will be performed over a range of possible radiuses until ‘good’ peaks are seen. This will give us a set of possible target locations. – After possible targets have been identified, a Procrustes method will be performed for matching each possible target with the known actual targets. Sobel Edge Detection • The Sobel Operator will be used for edge detection in the images, along both the horizontal and vertical (essential for detecting circles). • The Sobel Operator uses 3x3 kernels convolved with the original image. These kernels are: • The effect of these kernels is to calculate the approximations of the derivatives at each point which can detect abrupt changes from light to dark intensities. Sobel Edge Detection Results • • An example of Sobel Edge Detection on the imagery is shown below. The detection does a good job at capturing the edges of the targets while rejecting some of the other edged. It is noted that there is difficulty in detecting the targets obscured by shadows, which is acceptable if enough of the other targets are detected 2 7 3 9 8 -1 12 10 11 13 16 1 6 14 15 5 Circular Hough Transform • The Circular Hough Transform is a series of operations performed on black and white images for circle detection. • The general method of the transform is as follows: – Create an accumulator matrix the size of the image. – For every bright pixel in the image, draw a circle of radius R centered at this pixel in the accumulator matrix. In other words, increment the value of the accumulator matrix by one at every pixel where a circle is drawn. – Peaks in the resulting accumulator matrix indicate circles of radius R being present at that pixel in the original image. – An illustration: Circle in source image Red circles are those drawn around the source circle The accumulator develops a peak where the circles intersect, which is the center of the circle Circular Hough Transform Results 690 700 445 450 455 460 465 470 475 480 485 BW level image of Sobel Filtered Image Results of Circular Hough Transform The centers of the targets have noticeable peaks in them 710 720 730 740 Circular Hough Transform Results • Due to irregularities in the shapes and sizes of the target circles, the Circular Hough Transform may not produce a peak at the center of each target, but a small cluster of peaks near the center. In these cases, close peaks (on the order of less than a circle radius away) will be averaged together. 490 480 480 470 470 460 460 450 450 440 440 430 430 600 590 600 610 620 630 640 650 660 670 610 620 630 640 650 660 Circular Hough Transform Results • • • • • Original image with detected targets superimposed. Generally, the method is capable of picking up a large fraction of the targets. Some are not detected due to shadowing. Also, clutter around the target (tape round the top-most undetected target) can cause clutter in the detected edges, therefore a poor circle shape. However, further processing will allow for good object tracking even in when some targets are not identified. Method is very good at not erroneously identifying clutter as a target. Procrustes Method • Once potential targets are identified, the next step is to match them with the actual known targets. A Procrustes Method was employed to do this. • The Procrustes Method employed consists of several stages: – Pre-process the set of actual set of points by translating them in X and Y such that the mean position of all the targets is zero, and then scale them such that the RMS distance of all points to the origin is one. – For the identified points: • Translate the identified points in X an Y the same way as the actual points. • Scale the identified points the same way as the actual points. • Once the points are translated and scaled, finding orientation angle: – Iterate over a set of angles and calculate the distance from each identified target to the nearest actual target. – For each identified target, choose as it’s pair the actual target that is closest. – Calculate as a cost function the sum of the square of these distances. – The rotation angle to use is that which has the smallest value of the cost function. Procrustes Method • • An orientation angle is now known. However, because the identified targets may be missing some, the translation and scaling first performed may not have been accurate. Therefore, another round of translation and scaling is performed. – Translate the identified targets again in X and Y by sweeping over a small range of values, picking the one produces the minimum value of the cost function. – Likewise, scale the points again over a small range of values, again picking the scale value that minimizes the cost function. • • • Performing another round of rotation could be done, but generally by this point the identified and actual points will be close enough to not require it. At this point, each identified point can be matched with an actual point by finding the actual point that is closest to each identified point. Now, we have the pixel location in the image for each actual target, and similarly the orientation angle, the scale, and translation. Results Target Locations After Detection 2 Actual Target Positions and Labels Detected Targets Estimate 1.5 2 1 8 10 12 13 16 11 6 14 15 5 Normalized Y direction 3 9 1 15 15 55 0.5 11 11 14 14 17 8 66 0 18 -0.5 1 1 10 10 3 13 13 7 9 4 16 12 -1 2 -1.5 -2 -2 An example image with each detected target assigned a reference number corresponding to an actual target. -1.5 -1 -0.5 0 0.5 Normalized X direction 1 1.5 A plot of the normalized actual target locations with the locations of the detected targets after translation, scaling, and rotation. 2 Results • A subset of 50 images was selected and run through the described method. Fraction of Targets Detected Mean Distance from Detected Targets to Actual Targets Number of Detected Targets Rejected as No Good 1 0.9 8 0.2 0.8 Number of Rejected Detections 7 0.7 Mean Distance 0.15 Fraction 0.6 0.5 0.4 0.1 0.3 0.05 0.2 5 4 3 2 1 0.1 0 6 0 5 • 10 15 20 25 30 Image Number 35 40 45 50 0 0 5 10 15 20 25 30 Image Number 35 40 45 50 0 0 5 10 15 20 25 30 Image Number 35 An average of 74.4% of targets are identified, plenty to develop a good estimation of the rover position. 40 45 50 Conclusion • Performance is generally good, although some images are more difficult to track than others due to the reasons previously mentioned: shadowing, sun angle, etc. • The algorithm is efficient, taking a fraction of a second to complete per image with little optimization. • Room for improvements include performance optimizations and more thorough pose estimation based on roll/pitch/yaw characteristics.