Robotic system often use simultaneous localization and mapping method in their operations. Most of the calculation stored as a nested array with multiple level and dimension. SLAM data contains robot movement, object detection and relation between them. This system visualize SLAM data into a map containing robot historical position,object position and relation between object and robot that show detections line from each robot position. The visualized so human eye can understand it. This paper describes the process of movement and detection data composition and conversion to prepare the information required to build a map. The map composed by plotting every movements and detections into polar coordinate area. The map stored into a database for flexible future usage. Commonly used web based interface chosen to display the map via web browser. The map generated by server side scripts that transform polar data into full map

Dr. Achmad Widodo, ST, MT

Dr. Suryono, S.Si, M.Si

Harindra Wisnu, Pradana

English

Diponegoro University Institutional Repository

International Conference on Information Systems for Business Competitiveness (ICISBC 2013)  134  Visualization of Simultaneous Localization and Mapping using SVG Non Destructive Observations and Visualization of Robotic System  Harindra Wisnu Pradhana 1) Suryono 2) Achmad Widodo 3) 1) 2) 3) Magister Sistem Informasi Universitas Diponegoro Semarang, Indonesia email: harindrawisnu@gmail.com 1) suryono@undip.ac.id 2) awid@undip.ac.id 3)   Abstract—Robotic system often use simultaneous localization and mapping method in their operations. Most of the calculation stored as a nested array with multiple level and dimension. SLAM data contains robot movement, object detection and relation between them. This system visualize SLAM data into a map containing robot historical position, object position and relation between object and robot that show detections line from each robot position. The visualized so human eye can understand it. This paper describes the process of movement and detection data composition and conversion to prepare the information required to build a map. The map composed by plotting every movements and detections into polar coordinate area. The map stored into a database for flexible future usage. Commonly used web based interface chosen to display the map via web browser.  The map generated by server side scripts that transform polar data into full map. Keywords-component; SLAM, Robotic, SVG, Visualization. I. INTRODUCTION Simultaneous localization and mapping (SLAM) first announced on IEEE Robotics and Automation Conference in 1986. SLAM problem asks possibilities of generating map and simultaneously determining locations of a mobile robot dropped at unknown location in an unknown environment [3]. The SLAM problem is actually solved using several algorithms developed, but still there are many area of development available. The robot taking observations of several unknown objects in SLAM [3]. The object detections data contain its relative position from the robot. Along with the robot movement, the object relative position changes. Every detections data processed to estimate the exact position of the objects. Reference [2] show that the movement of the robot can can be represented by a vector with the angle and distance of the position of the robot at one time relatively from the previous position and the next position relatively from the current position. The object detection can also represented as a vector that store the distance and angle of the object position relatively from the current robot position. The essential SLAM problem is displayed in Fig. 1.  Figure 1.  The essential SLAM problem. SLAM research in the past decade focusing on process efficiency while maintaining precision [5]. In order to reduce computation complexity and memory a large scale environment map can be split into several smaller maps [4]. SLAM observation resulting movements and detections data processed and stored into local map containing every object relative position and the robot historical position. All the local maps later can be composed into single large scale global map. This research observes existing SLAM system using non-destructive method in order to visualize the SLAM system to be understood by human eye. The system got it's input from the slam movement and detections data and process it to generate a map. The system checks for data consistency, once there data inconsistency found, the system then generate a new map separated from the previous one. Various data source can be used such as SLAM databases, log file, or using various interfacing method with the SLAM system. II. DATA PREPARATION This research capable of building maps from many interface method. Different SLAM system creates different data structure. To ease the process, this system prepares the input data using several data composition method: International Conference on Information Systems for Business Competitiveness (ICISBC 2013)  135   Data extraction from the SLAM systems, can be file reading, database query or other interfacing method.  Unit conversion from the SLAM system only if required to store the data in metric system.  Data cleaning to ignore system message or redundancy record so only the required data stored. Data cleaning also held to skip unused column from the record.  Data consistency checked in order to determine is the current record related with the previous record in order to store the data as the same map data or a new map data. This research use simulation to test processing SLAM data. Simulation data prepared using random object placement in two dimension area. The observation area and the robot movement plans displayed in Fig. 2.  Figure 2.  Simulation map Fig. 2 display several object randomly placed in the map as red square. Simulation held four times with different robot starting point displayed as yellow triangle in the map. The movement and detection data measured and stored into a database. The data stored then processed using data preparation method and stored separately as movement data and detection data for the next process.  III. MOVEMENTS AND DETECTIONS RECAPITULATION The next process is recapitulation. The movement and detection data processed separately. The movement data recapitulation resulting historical robot position for each map while the detection data recapitulation resulting estimated object position for each map. Robot and object positions are represented in polar coordinate system. A. Movements Recapitulation Movement recapitulation process can be done using simple vector addition concept that the final robot position is the summary of the movement vector and the previous position vector [2]. The robot orientation changes in every movement. The final orientation of the robot is the summary of every rotation movement [2]. ForA⃗n is the position of robot A at step n with two vector componentln andαn while βn is the orientation of robot A movement recapitulation process described in (1), (2) and (3). l n=√(∑x= 1x= nl x sin θ x)2+ (∑x= 1x= nl x cosθ x)2 (1)θn= tan−1(∑x= 1x= nl x sin θx )(∑x= 1x= nl x cosθ x) (2)βn=∑x= 1x= nθ x (3) Tangent operations cycle resulting the same value for every 1800 that arctangent operations might resulting two different angle. In order to get the correct angle, opposite and adjacent analysis required. B. Detections Recapitulation Detections recapitulations resulting object estimated position. Using vector concept the position of the object are the resultant vector of robot position and the detection vector. The vector concept of object detection is displayed in Fig. 3 below.  Figure 3.  Object detection resultant vector International Conference on Information Systems for Business Competitiveness (ICISBC 2013)  136  For A⃗n is the position of robot A at step n,D⃗mn is the detection vector of object m at step n with component vectorlmn andαmn , O⃗m is the estimated position of object m with component vector l m andαm object recapitulation process described in (4) and (5). lm= √(l n cosαn+ lmn cos( βn+ αmn))2+ (l n sinαn+ lmnsin (β n+ αmn))2 (4)αm= tan−1 [ l n sinαn+ lmn sin( βn+ αmn)][ l ncosαn+ lmn cos( βn+ αmn)]  (5) Object detections in SLAM system stored as relative value from the robot position including the orientation of the robot. To get the real vector, the detection angle required to be added with the robot orientation. As the movement data recapitulation, opposite and adjacent analysis required in detection data recapitulation. IV. MAP GENERATION The map generated by plotting every movement historical data and every object estimated position into drawing area. The map generations consist of two steps that are data composition and map plotting. A. Map Data Composition Before plotting into drawing area, the map data need to be composed from the previously explained recapitulation process. The data composition's main goal is to compose a standard form of array variables containing every movement and detection information required to generate the map. For array A containing movement data and array O containing object data, array X with map data described in (4).   X = [AO]  Array of movement data contain historical position data from the first step to the last step notated asAn described in (5) while arrayAn containing robot position at step n described in (6) A= [A1 A2 ... An]  An= [lnα nβn]  Array of object data contain every estimated object m described in (7) while Om containing object's estimated position and array of detection history described in (8). O= [O1 O2 ...Om]  Om= [lmαm Dm]  ArrayDm contain every detection of object m at step n described in (9). Array Dmn contains relative detected object position described in (10). Dm= [ Dm1 Dm2 ... Dmn]  Dmn= [ lmnαmn]  B. Map Plotting The plotting progress transforms array data from the previous section into two different map that is simulation map and local map. Both map display robot movement and mark every historical position of the robot. The simulation map display object detections in every step while the local map displays the estimated object location. Map plotting process held using SVG that has capability to display vector graphic shapes, images and texts [1]. SVG use vertically flipped Cartesian coordinate system. Vector shapes in SVG formed inside XML tags with its attribute and variables. Robot movement represented as vector path while objects positions represented using circle shapes and the robot position represented using triangle shapes. SVG script generated from first simulation resulting simulation map displayed in Fig. 4 below.  Figure 4.  Simulation map from first data Fig. 4 above display the robot movement as blue dashed line connecting blue triangle that represent robot historical position. The red circle display object position when detected by certain position connected with red dashed line. The local map also generated that display the estimated object position displayed in Fig. 5.  International Conference on Information Systems for Business Competitiveness (ICISBC 2013)  137   Figure 5.  Local map from first data Fig. 5 above also displays red dashed line to represent relation between objects. The relation line displayed to illustrate spring network analogy between objects. The rest of the map from second to fourth simulation displayed in Fig. 6, 7, and 8. V. CONCLUSION Visualization can be made to SLAM system using simple few steps. Data composition process required to prepare data into standard form to be visualized. Robot movement and object detection recapitulation process can be done using simple vector resultant concept by adding one vector into another. ACKNOWLEDGMENT The researcher would like to thank W3 group for proposing such a simple yet powerful vector graphic tools capable to visualize every aspect of a vector. REFERENCES  [1] E. Dahlstrom, et al, "Scalable Vector Graphics 1.1 Second Edition," W3C Recommendation, 2011. [2] H.W. Pradhana, Suryono, and A. Widodo, "Web based map generations of mobile robot movement using scalable vector graphic," Proceedings of The 1st  Conference on Information Technology, Computer, and Electrical Engineering, 2013. [3] H. Durrant-Whyte and T. Bailey, "Simultaneous localization and mapping (SLAM) Part I : the essential algorithms," IEEE Robotics & Automation Magazine, Vol 13 issue 2, pp. 99-110, 2006. [4] K.S. Chong, and L. Kleeman, "Feature-based mapping in real, large scale environments using an ultrasonic array," International Journal of Robotic Research. Vol 18 no 1, pp. 3-19, 1999. [5] T. Bailey and H. Durrant-Whyte, "Simultaneous localization and mapping (SLAM) PART II : state of the art," IEEE Robotics & Automation Magazine, Vol 13 issue 3, pp. 108-117, 2006.   Figure 6.  Simulation map and local map from second data International Conference on Information Systems for Business Competitiveness (ICISBC 2013)  135    Figure 7.  Simulation map and local map from third data   Figure 8.  Simulation map and local map from fourth data 

Visualization of Simultaneous Localization and Mapping using SVG

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Visualization of Simultaneous Localization and Mapping using SVG

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