Patent Application: US-201213594962-A

Abstract:
localization and tracking system . the system includes at least one laser mounted for pointing its beam at arbitrary locations within a three - dimensional space . an object within the three - dimensional space supports a screen at its top for receiving the laser beam to create a shaped image on the screen . the shaped image on the screen is observed by a camera and computing apparatus determines the location of the object in the three - dimensional space from pointing parameters of the laser and from shape and center points of the shaped image on the screen .

Description:
the tracking system disclosed herein is based on lasers that are tracked with a camera system . opposed to the concepts described earlier , this system combines them by using a camera setup on top of the robot facing upwards . the laser is tracked while it hits a screen positioned above the camera and the signal is used to generate the tracking information . a two axis gimbal 75 system guides the laser anywhere inside the tank . certain translational and rotational information can be derived from this system and allows local and global , non - incrementing localization of the robot . fig1 a shows the overall concept of the tracking and localization system . a gimbal system , shown to the right of the robot , consists of two rotating axes . we will call the axis 80 described by the angle θ “ major axis ” and the one described by σ “ minor axis ”. the laser is attached to the minor axis and thus can be directed anywhere inside the lng / lpg tank . the laser beam targets the screen on top of the camera at an angle of δ while the robot is facing along the y - axis . fig1 b shows a model of a rigid body to indicate the robot &# 39 ; s six dof . indicated are the translational positions x , y and z as well as the rotational positions ψ ( pitch ), ω ( roll ) and φ ( yaw ). control of x and y is needed to move the robot at the bottom of the tank . since the robot also needs to inspect some of the lower part of the tank &# 39 ; s wall , control over z is needed . the yaw can be obtained by using gyroscopes ; unfortunately they are prone to noise . the following discussion outlines the potential information that can be obtained from different laser configurations , by either using different beam shapes or by using two or more lasers . fig2 shows a selection of potential created shapes on top of the detector screen . the ( lashed lines indicate the symmetric lines of each shape inside the blocks ( a ) to ( i ). the created shapes on the screen with respect to a defined center point yields information such as the direction and speed of the robot motion . from a spot - shaped laser ( fig2 a ) a tracking / localization signal for the desired x and y positions of the robot can be generated . obtaining the yaw information is not possible using this configuration . since laser beams can have different shapes , fig2 ( b ) indicates a square that has four symmetric lines . again , this case only allows similar ability tracking as the spot shape . fig . ( 2 c ) shows a half circle and could be created by blocking half of a spot - shaped laser beam . tracking in x and y is possible , but the yaw is unique . this gives the robot the opportunity to gain information about its yaw inside the tank area with respect to the gimbal system thus offering global yaw localization . as indicated , only a single symmetric line exists in a half - circle in contrast to the previously discussed shapes . by inspecting fig . ( 2 d ) a rectangular shape can track x and y , but no information about the yaw can be obtained . we conclude that tracking the translational positions x and y does not require certain beam shapes and applies to all of the following shapes . in addition , one or less symmetrical lines within the shapes yield to a unique interpretation of the yaw . two laser beams of any shape reduce the symmetry of the resulting shapes to no more than two symmetric lines , as can be seen in fig2 ( e )-( i ). splitting the rectangular beam in fig2 ( d ) resulting in 2 ( e ) contains the same amount of information as before . by not splitting the rectangular beam exactly along one of its symmetric lines , two different shapes are generated , meaning the yaw can be determined uniquely . two laser beams can allow the detection of the pitch , roll and z . when these variables change , the screen cuts the laser beams by different angles . this results in different distances of the single shapes from each other . reducing symmetries can be achieved not only by using different shapes , but also by colors . using two different colored laser beams , as shown in fig2 ( i ), always leads to an unique interpretation of the yaw . also , this is independent of the beam shapes . for the remainder of this patent application we will use the two laser beam configuration with different colors . later , it becomes clearer why this configuration , in particular , is advantageous . also , the red laser spot will be used for tracking / guiding the robot in respect to the center point . in combination with the green laser , the discussed localization information is obtained . we will now introduce a two dof gimbal system with the attached lasers , representing yaw and pitch movements . we will discuss aspects to be considered as well as limitations of the system . the gimbal allows pointing the laser anywhere inside a 3 - dimensional object and detailed dynamic models have been developed in the past [ 1 , 2 ]. as an example , gimbal systems have also been used for laser communication systems on airplanes [ 3 ]. the configuration of the gimbal system is important to consider in order to achieve maximum benefit from the previous discussion on beam shapes . in fig3 ( a ), the gimbal is standing on the same plane on which the robot is moving . while directing the gimbal the two laser beams form circular movements , keeping the same ( red ) laser always closer to the gimbal center point . an area might exist where the gimbal cannot point to as indicated by a gray spot . fig3 ( b ) is a similar case where the gimbal is mounted above the tracking area . the gray zone disappears and the gimbal is able to cover the whole area . fig3 ( c ) turns the gimbal by 90 ° and the laser beams maintain a constant angle , thus always facing the same direction . based on this analysis , one can measure the angle β formed by the horizontal line and a vector connecting the two laser spots , as shown in fig4 . β can be obtained directly for the case in fig3 ( c ) and needs to be corrected for the two cases in fig3 ( a ) and ( b ) as follows : the red laser spot is always located between the green spot and the center point . by defining the coordinate system of the robot and the gimbal to have the same orientation , thus e . g . 0 ° faces the exact same direction , the following equation gives the proper global yaw of the robot : the minimum rotating step size of the gimbal also determines the minimum step size that the laser can be moved on the detector screen , as shown in fig4 . s x and s y are the minimum steps in x and y , respectively , and s is the combined minimum step . the relationships can be described according to fig5 . h 1 represents the actual beam length and h 2 the intended beam length at the new laser position , which is dependent on the actual position of the laser due to the angle between laser beam and plane . we define s total , xy = s xy + s 0 , xy and the distance s xy can be described by trigonometric functions as follows : to obtain the minimum step size in the 2d - plane , s can be calculated as : s =√{ square root over ( s x 2 + s y 2 )} ( 4 ) for example , by using a sick dfs60a incremental encoder with 65535 lines / rev , the minimum angle increment is 0 . 00551 °. by letting h 0 be the tank height at 60 m and the laser beam at position s 0 = 50 m , the angle δ is 39 . 8056 °. this results in a minimum s x and s y step of approximately 1 cm and an s of 1 . 41 cm . localization of as many dof as possible are desired to gain the best knowledge of the robot &# 39 ; s position and movement inside the tanks . the described system allows access up to four dof , both in a local and global sense . this will be described in more detail as follows . localization in the local sense means that the robot obtains information in respect to its own position , but not in respect to its environment such as a gas tank . as discussed earlier , translational movements x l and y l are possible to detect in relation to the detection screen center point . the yaw φ l is defined in respect to the 0 ° angle of the robot only . global localization offers information about the robot in respect to its environment . the global translational movements x g and y g are possible to detect , but the gimbal angle information of both axes is needed to determine the unique position of the robot . for accuracy reasons , the laser with the distance sensor can be taken into account additionally . depending on configuration of the gimbal system the global yaw φ g is the same as the local yaw φ l . if that is not the case , then the angle θ of the major gimbal axis needs to be taken into accounted . global translational movement z g is uniquely measurable in certain circumstances and if they are met , the robot can be tracked while it is possibly climbing up the walls . for a given tank shape , the distance measurement from the laser and the angle information from the gimbal must be available . the system then knows when it has to expect a wall and the gimbal needs to move a different tracking pattern to guide the robot . the camera speed is a crucial performance factor of the system . a standard commercial camera , like a web - camera , offers frame - rates up to 30 frames / s at lower resolutions ( e . g . 640 × 480 ). more advanced cameras have higher rates with several thousand frames / s [ 4 ], offering performance for demanding control , tracking and localization problems . the detector plane on top of the robot must have a certain size that depends on the minimum step size of the gimbal system . based on our previous calculations , a detector screen of size 30 cm × 30 cm has been chosen . since the camera images are not quadratic due to common sensor dimensions , the larger side can be cropped to form a 480 × 480 image . for simplicity , it has been assumed that the pixels are quadratic and organized side by side , as shown in fig6 . at this particular resolution , each pixel covers an area of 0 . 625 mm 2 . a laser beam with a diameter of 10 mm on the detector screen results in an illuminated area of πr 2 = 78 . 5 mm 2 . this area is then covered by around 230 pixels , which is sufficient for recognizing its shape . the covered size of the detector screen can be arbitrarily adjusted by adjusting the distance from the lens to the screen or by using lenses with different focal lengths , keeping in mind that the pixel size scales linearly with the range . the dynamic range is particularly limited with cameras based on charge coupled devices ( ccd ) sensors . lasers are very bright compared to surrounding light conditions , hence the exposure times need to be calibrated accordingly . exposure times tend to be very short , resulting in high image capture rates . still useful images can be easily taken with an exposure time of 1 / 30 s or less . this means , that a frame rate of 30 frames / s or more can be achieved . as can be seen later in the experiments , background noise does not cause an issue in this setup . the experiments are conducted at artificial lightning conditions , but due to the very short exposure times , everything except the laser spots appears black . thus , the system is insensitive to background noise . we will use a red and green laser for tracking and localization . the necessary steps to detect these spots are presented in this section . while the laser beams are hitting the detection screen , they are imaged with the previous described commercially available web - camera . the laser spots , which represent the reference signal for a robot control algorithm , deliver a new value approximately 33 ms each , sufficient for our tracking and control problem . proven and basic image processing algorithms [ 5 ] are applied to detect the laser beams shapes and their corresponding center points . in general , the scheme is shown in fig7 , where the numbering corresponds to the following steps : 1 . obtaining an image from the camera , 2 . splitting of the image into its different color channels resulting in three 8 - bit gray scale images , 3 . usage of a low pass filter ( e . g . gaussian ) to suppress random noise , 4 . forming of a binary image by setting a threshold ( all values above or equal the threshold are set to one and zero otherwise ), 5 . detection of edges of the binary image ( e . g . by a sobel edge detector ), that results in a circle representing the binary spot , 6 . fitting of an ellipse to the data points , giving center , orientation and parameters of the ellipse , 7 . combination of all the data to obtain the desired information for use by the robot control system . this scheme is repeated after the last step . since we have chosen a green and a red laser , step 2 is very convenient , because of the split into red - green - blue ( rgb ) color channels . as fig7 shows , steps 3 to 6 can be solved in parallel . this offers the use of field programmable gate arrays ( fpga ), that can implement truly parallel executed processes . this is in contrast to microprocessors that can only execute one task at a time , even if programming languages like national instruments ( ni ) labview are used and parallelism is implied . the ellipse fit in step 6 has been chosen for two reasons . first , the circular spot of the green laser will be shaped like an ellipse when the angle between laser system and robot is other than 90 °. at 90 °, the laser forms a circle , which is a special case of an ellipse . second , the rectangular beam of the red laser forms similar to an ellipse on the detector screen . different approaches for fitting ellipses have been developed in the past , such as the method by taubin [ 7 ], a convolution method by zelniker et al . [ 8 ] and the direct least squares fitting of ellipses algorithm as described by fitzgibbon et al . [ 6 ]. this work uses the latter , since it is considered more robust and efficient than e . g . the taubin method . the implicit equation of an ellipse is described as : and the used algorithm estimates the coefficients a , b , c , d , e and f . the algorithm returns the center point in x and y , the radii and the angle that it is tilted . depending on the controls implementation of the robot , this information can be used for tracking in two ways : taking the absolute values of x and y and subtracting them from the center point coordinates x 0 and y 0 of the detector screen to obtain the control error e x and e y . fig4 shows the center point with respect to the red laser spot ; convert the laser spot coordinates into polar coordinates . the error e is now delivered through the distance from the center point to the red laser spot , as indicated by the arrow l 1 fig4 . the angle α ( fig4 ) thus gives the orientation . the experiments are carried out on single images to demonstrate the detection algorithm and its ability to derive the coefficients of ellipses . as for the reasons described earlier , two lasers of two different colors are used , red and green . the red laser is a sick dt500 with a wavelength of 650 nm that also incorporates a distance sensor . the green laser is a standard laser module from instapark with a wavelength of 532 nm . both lasers are fixed to each other and the beams parallel aligned . fig8 shows the prototype mobile robot for the development of the tracking , localizing and controls techniques . for the following experiment , the lasers are aligned perpendicular to the detection screen in a distance of 3 . 8 m ( angle δ in fig1 at 90 °). the green laser spot has a diameter of 6 mm on the screen and the red laser forms an ellipse of 4 mm in the major axis and 3 mm in the minor 255 axis . the distance between the two laser beams is 17 mm . fig9 shows a series of images during this process and starts with the original captured image in fig9 a . fig9 b , fig9 c , and fig9 d show the blue , red and green channel of the rgb image , respectively . as can be seen , the green and red laser conveniently divide into the respective rgb channels . the blue channel does not contain useful information and is ignored in the further procedure . following steps are a gaussian smooth function and conversion into binary images of the red and green channels , as can be seen in fig9 e and fig9 f . a sobel edge detector is applied and results in images shown in fig9 g and fig9 h . ellipses are fit to the edges of the previous result and both are plotted in fig9 i and fig9 j . fig9 k combines the original image with the result from the ellipse fit ( in yellow color ), delivering the center points and coefficients . the laser spots are close to circular , but due to the robot &# 39 ; s movement , the angle δ is usually different from 90 and ellipses are formed . the following demonstration shows the detection of these shapes and the figures will combine original captured images with the fitting result . in fig1 a the laser is aligned at δ = 45 ′ with respect to the detector screen . fig1 b and 10 c use alignment angles of 30 ° and 15 °, respectively . the images also indicate that the spots are spreading apart from each other due to the decreasing angle δ . the laser detection system has been implemented using ni labview 2010 and ni vision tool - box 2010 that is executed on a ni pxi - 1042 with build - in ni pxi - 8105 embedded controller . this patent application introduces a novel technique in tracking and localization of mobile robots in a hazardous environment , where different techniques cannot or are not allowed to be used . the tracking concept utilizes standard components and proofed detection techniques to find the parameters of laser beam shapes on the robot screen . the choice of red and green lasers turns out to be very handy , since the two lasers can be easily differentiated by splitting the captured images into their rgb color channels . combining the information of the robot laser 280 detection , the gimbal angles and the laser distance measurement allow local and global localization of the robot in the four dof x , y , z , and φ . the references listed herein are incorporated into this patent application by reference . equations of motion for a two - axes gimbal system yoon , sugpil ; 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