Margrit Betke and Leonid Gurvits. Mobile Robot Localization using Landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, April 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....it is a general method to use landmarks for localization. To perform reliable navigation, artificial patterns are often used as landmarks[1] However, it is a reluctant work for us to arrange artificial landmarks. Visual features such as vertical edges of door or obstacle are used as landmarks[2, 3]. However, such features may not be stable, that is, may not be observable under various conditions of lighting and background scene changed by viewpoints[4] Therefore, it is desirable to determine stable landmarks based on observed data. Proc. of IROS 99, pp. 781 786, Kyongju, Korea, Oct. ....

M. Betke and L. Gurvits, "Mobile Robot Localization Using Landmarks," IEEE Trans. on Robotics and Automation,Vol. 13, No. 2, pp. 251-263, 1997.

....and communication capabilities to interact directly with other robots. A module for absolute localization in known environments was considered and is currently under development. That module will use the eld natural landmarks, such as the goals and the corners to determine the robots localization [12]. Figure 5.2 a) and b) present front and rear photos of one of the built robots. In a) it it is possible to see the CPU casing, the CCD camera, the front battery compartment and the kicker. In b) the wireless Ethernet model and the wheel encoders are visible. Besides the hardware architecture, ....

Margit Betke and Leonid Gurvis. Mobile Robot Localization using Landmarks.

....based on computer vision, range finders or other sensors, are computationally expensive and not too robust. So, they are being used in research centres but are largely uncommon in industrial or service applications. Location through marks or beacons is usually preferred for these real applications [1,4,8]. The general principle is as follows: sets of marks or beacons are placed in known positions of the environment, and are detected by an optical system. The robot position is then obtained from a simple triangulation algorithm. Usually, laser telemeters [9,10] are employed for obtaining the ....

Betke, M and Gurvits, L., 1994, "Mobile Robot Localization Using Landmarks." 1994 International Conference on Intelligent Robots and Systems (IROS'94). Munich, Germany, Sept. 12-16, pp.135-142.

....reduce the dependency on the environment geometry. Although fiducial methods are potentially more robust, the number and locations of the fiducials can significantly affect accuracy. Existing techniques often focus on deriving pose estimates from a relatively sparse number of (noisy) measurements [4, 6, 9, 18, 21, 27]. For large arbitrarily shaped environments, such as the ones presented in this article, there does not exist a method for determining the optimal number of fiducials or their optimal placement in order to achieve a desired pose accuracy. In this article, we present a robust camera pose algorithm ....

M. Betke, L. Gurvits, "Mobile Robot Localization Using Landmarks", IEEE Transactions on Robotics and Automation, Vol. 13, No. 2, pp. 251-263, 1997.

....high resolution frames to disk and recovering camera pose with an average error of 0.56 in a region 15 feet in diameter. 1. Introduction Recently, we have seen a surge in the use of omnidirectional cameras for applications such as telepresence, 3D reconstruction, and autonomous navigation [3, 4, 17, 18, 20, 21, 22, 23]. Omnidirectional images can be created by several approaches [9, 10, 11, 12, 13] Nayar [13] proposes a catadioptric system for producing omnidirectional images with a single center of projection (COP) In this design, we view a convex paraboloidal mirror (i.e. the parabola s focal point is ....

M. Betke, L. Gurvits, "Mobile Robot Localization Using Landmarks", IEEE Transactions on Robotics and Automation, Vol. 13, No. 2, pp. 251-263, 1997.

....but rather temporary and ad hoc deployment. These networks would not justify the cost of setting up an infrastructure to support positioning, like proposed in [10] 4] or [2] The orientation and positioning problems have been extensively studied in the context of mobile robot navigation [11]. However, many methods proposed by the robotics community make extensive use of image processing and preset infrastructure, such as recognizable landmarks. Our aim is a positioning method that is robust, but relies on less computational resources and infrastructures. The rest of the paper is ....

....points. For small numbers of landmarks, the has a similar CPU complexity as the one. However, the problems solved are all of size 3, thus requiring much less memory, whereas the approach needs to handle x2 sized matrices. A solution linear in the number of landmarks n, proposed in [11], makes efficient use of the representation of landmarks as complex numbers. In our simulation we used the simple implementation, as it gives the same quality estimates as the linear solution presented in [11] but it is much more simple to implement and has a low penalty for small n. ....

[Article contains additional citation context not shown here]

M. Betke and L. Gurvitis, "Mobile robot localization using landmarks, " in IEEE International Conference on Robotics and Automation, vol. 2, May 1994, pp. 135--142.

....and their actual position during the replay step. A standard localization method is to measure angles between landmark positions and to compute the pose from intersections of the circles that represent the possible positions from each possible pair of beating measurements [14] 10] 6] [2]. We extend this idea by measuring both azimuth and elevation angles of a landmark to represent its position on a sphere instead of a circle (Fig. 1) There have been a number of papers on the process of selecting useful features points or natural markers in image data [7] 8] 9] 11] 12] ....

M. Betke and L. Gurvits. Mobile Robot Localization Using Landmarks. IEEE Trans. on Robotics and Automation, 13:251 263, 1997.

....localisation and a number of methods have been proposed for achieving accurate localisation given maps of the environment in which the vehicle operates. Beacon based navigation schemes rely on taking observations of beacons whose position in the robot s environment have been surveyed beforehand [6, 8, 31]. These observations can be fused using an appropriate filter framework, such as the popular Kalman filter, to provide real time updates of vehicle position with bounded position error. Recent work by Thrun [24, 55, 63] has shown that accurate, robust, real time localisation can be achieved in ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251-263, 1997.

....thus driving the robot toward a nearby attractor, and lowering the homing precision. Possible solutions to this problem are under investigation. For example, one can assess the coherence of the set of contribution vectors and ignore matchings that create inconsistencies, taking inspiration from [26]. This would mean that objects with variable positions would be ignored by the navigation system as unreliable landmarks. Finally, it is worth mentioning that the short range navigation strategy that was implemented here did not involve dedicated sensors measuring distances, but rather relied on ....

M. Betke & K. Gurvits, Mobile robot localization using landmarks, Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 1994, 135142.

....but rather temporary and ad hoc deployment. These networks would not justify the cost of setting up an infrastructure to support positioning, like proposed in [5] 6] or [7] The orientation and positioning problems have been extensively studied in the context of mobile robot navi gation [8]. The methods proposed make extensive use of image processing and preset infrastructure, such as recognizable landmarks. What is necessary for ad hoc deployment of temporary networks is a method similar in capability to GPS and magnetic compasses, without requiring extra infrastructure, or ....

....points. For small numbers of landmarks, the ( has a similar CPU complexity as the ( one. However, the problems solved are all of size 3, thus requiring much less memory, whereas the ( approach needs to handle 4x2 sized matrices. A solution linear in the number of landmarks n, proposed in [8], makes efficient use of the representa tion of landmarks as complex numbers. In our simulation we used the simple ( implementation, as it gives the same quality estimates as the linear solution presented in [8] but it is much more simple to implement. A second way of positioning using ....

[Article contains additional citation context not shown here]

M. Betke and L. Gurvitis, "Mobile robot localiza- tion using landmarks", in Proceedings of the IEEE International Conference on Robotics and Automation, volume 2, pages 135 142, May 1994.

....Awerbuch. An extended abstract will be published in the Proceedings of the 1995 Conference on Computational Learning Theory [3] It is also published as an MIT Laboratory for Computer Science technical memo MIT LCS TM 516 in January 1995 [4] Chapter 3 presents my joint work with Leonid Gurvits [16]. The work was accepted as a regular journal paper at the IEEE Transactions on Robotics and Automation in February 1995. An extended abstract of this paper is published in the Proceedings of the IEEE RSJ GI International Conference on Intelligent Robots and Systems, September 1994, and also as a ....

....by itself. Automatically recognizing traffic signs in images is very valuable for mobile robot or autonomous vehicle navigation. A robot that can recognize a traffic sign as a familiar landmark in its map of the 96 environment can then use this information to localize itself in its environment [16, 22]. Our method stands apart from previous approaches to traffic sign recognition because first, it is efficiently applied to real world landscape images (as opposed to Ettinger s isolated signs [44] and second, it does not rely on color perception which is very sensitive to lighting changes. This ....

Margrit Betke and Leonid Gurvits. Mobile robot localization using landmarks. In Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, pages 135--142, Munich, Germany, September 1994.

....called structure from motion [2, 14] If we know the 3 D shapes of the objects or the 3 D structure of the scene that we are observing, we can compute the 3 D camera position from a single image. This problem has been studied by many researchers as self localization for mobile robot applications [1, 5, 11, 12]. Recently, techniques for computing both the camera parameters and the 3 D positions of the camera by observing multiple images of the same scene about which we have no prior knowledge have inten sively been studied [8, 14] Such a technique, known as self calibration, may be very useful in ....

M. Betke and L. Gurvits, Mobile robot localization using landmarks, IEEE Trans. Robotics Automation, 13-

....called structure from motion [2, 16] If we know the 3 D shapes of the objects or the 3 D structure of the scene that we are observing, we cm compute the 3 D camera position from a single image. This problem has been studied by many researchers as self localization for mobile robot applications [1, 6, 13, 14]. Recently, techniques for computing both the camera parameters md the 3 D positions of the camera by observing multiple images of the same scene about which we have no prior knowledge have intensively Broadcast Division, FOR A Co. Ltd. 2 3 3 Ohsaku, Sakura, Chiba 285 0802 Japan, Tel: ....

M. Betke and L. Gurvits, Mobile robot localization using landmarks, IEEE Trans. Robotics Automation, 13-2 (1997), 251 263.

....has the advmtage that it can be adapted to mismatch removal: we pick out multiple minimal sets of data and choose the solution supported by majority voting [4] For assumed matching, however, it is obviously better to fuse all available in formation in an optimal manner. Such a method also exists [2], but so far the main concern has been methods for estimation; little attention has been given on theoretical optimality and reliability of the solution. The aim of this paper is not to propose yet mother new solution technique. Rather, we focus on statistical aspects. We first introduce a model ....

M. Betke and L. Gurvits, "Mobile robot localization using landmarks," IEEE Trans. Robotics Automation, 13-2 (1997), 251 263.

....this task is to put landmarks in known locations in the workspace. In any place in the workspace the robot is able to measure the bearings to a sufficient number of these landmarks. Using three or more such measurements the robot is able to estimate its position and orientation in the plane [19, 17, 10, 5]. The most widely used method for computing this estimate is a geometric method based on the idea that the angle between two such bearing measurements yields the constraint that the robot s position is limited to lie on a circle. Adding an additional bearing measurement yields two more circles ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Trans. on Robotics and Automation, 13(2):251--263, 1997.

....an extensive library that makes precise object localization possible. The whole procedure implemented in the RoboLog kernel is able to work even when only little or inconsistent information is given. In particular, we employ the method for mobile robot localization using landmarks stated in [1]. In our system, the following basic skills (among others) are implemented: # The agents can search for the ball, taking into account their knowledge about the last time the ball was seen. 118 # Dashing and kicking to a certain position, regarding the agent s condition and avoiding obstacles is ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, Apr. 1997.

....an extensive library that makes precise object localization possible. The whole procedure implemented in the RoboLog kernel is able to work even when only little or inconsistent information is given. In particular, we employ the method for mobile robot localization using landmarks stated in [4]. 2.2 Basic Skills Agents have to be able to move in their environment without collision. This is a basic requirement for many practical robot multi agent systems. In the RoboCup scenario agents should also be able to handle the ball. This means they must be able to run and kick to a certain ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, Apr. 1997.

....frame of reference is helpful in order to communicate with other agents in situations where cooperative actions are appropriate. The RoboLog system hosts several different procedures for self and object localization wrt. an external coordinate system. The first method, that is introduced in [BG97] requires (only) three or more directions to visible landmarks relative to the orientation of the agent to be known. Provided that at least three of them and the position of the agent neither form a circle nor lie on a straight line, the absolute position and orientation of the agent can be ....

Margrit Betke and Leonid Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, 1997.

....frame of reference is helpful in order to communicate with other agents in situations where cooperative actions are appropriate. The RoboLog system hosts several different procedures for self and object localization wrt. an external coordinate system. The first method, that is introduced in [4] , requires (only) three or more directions to visible landmarks relative to the orientation of the agent to be known. Provided that at least three of them and the position of the agent neither form a circle nor lie on a straight line, the absolute position and orientation of the agent can be ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, Apr. 1997.

....recognition, which allow robot position to be calculated. 1,10,21] Triangulation and trilateralization systems. These solutions use three or more tags, previously placed in known positions of the environment. A robot can then determine its absolute position, upon relative to tags position. [2,5,8]. Map matching systems. Map based systems determine robot position by comparing sensor data, and data obtained from a previously built representation of the environment. Several solutions have been proposed for environment representation. One of them consists of using grid cells, which allows a ....

Betke, M. And Gurvits, L., "Mobile Robot Localization using Landmarks",, IROS94,Munich 1994, Sept 12-16, pp 135-142.

.... technique is to select a collection of landmarks in known positions and inform the robot beforehand [30,43,32] Another technique is to let the robot select its own landmarks according to a set of criteria that optimize its ability to localize, and then use those landmarks to correct its position [6]. The second approach to localization is to perform a matching of the sensor data collected at the current location to an existing model of the environment. Sonar, and laser range nder data have been matched to geometrical models [42,44,45,68,48,50] and images have been matched to higher order ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Trans. on Robotics and Automation, 13(2):251-263, April 1997.

.... [14, 25, 30, 44, 45, 50, 55, 57, 70] and various chapters in [40] While some localization approaches, such as those described in [31, 41, 62, 34] localize the robot relative to landmarks in a topological map, our approach localizes the robot in a metric space, just like those methods proposed in [2, 65, 68]. Almost all existing approaches address single robot localization only. Moreover, the vast majority of approaches is incapable of localizing a robot globally; instead, they are designed to track the robot s position by compensating small odometric errors. Thus, they differ from the approach ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. Technical Report SCR-94-TR-474, Siemens Corporate Research, Princeton, December 1993. will also appear in the IEEE Transactions on Robotics and Automation.

.... 85, 99, 105, 110, 131, 148] and various chapters in [76] While some localization approaches, such as those described in [62, 78, 132] localize the robot relative to some landmarks in a topological map, Minerva s approach localizes the robot in a metric space, just like those methods proposed in [10, 141, 144]. The vast majority of approaches is incapable of localizing a robot globally or to recover from robot kidnapping. Instead, they are designed to track the robot s position by compensating small odometric errors [56, 57, 89, 120, 134] Recently, several researchers proposed the Markov localization ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. Technical Report SCR-94TR -474, Siemens Corporate Research, Princeton, December 1993. will also appear in the IEEE Transactions on Robotics and Automation.

.... [14, 25, 30, 44, 45, 50, 55, 57, 70] and various chapters in [40] While some localization approaches, such as those described in [31, 41, 62, 34] localize the robot relative to landmarks in a topological map, our approach localizes the robot in a metric space, just like those methods proposed in [2, 65, 68]. Almost all existing approaches address single robot localization only. Moreover, the vast majority of approaches is incapable of localizing a robot globally; instead, they are designed to track the robot s position by compensating small odometric errors. Thus, they differ from the approach ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. Technical Report SCR-94-TR-474, Siemens Corporate Research, Princeton, December 1993. will also appear in the IEEE Transactions on Robotics and Automation.

....vary substantially, depending on the sensors, their geometric models and the representation of the environment. Sugihara [15] considered two cases of point location problems, and [10] followed this approach and formulated the positioning problem as a search in a tree of interpretations. In [4], an algorithm is proposed for localization based on ray angle measurement using a single camera, and then in [5] an odometric sensor was added to landmark based ray measurements and an extended Kalman filter was used to combine vision and odometric information. A method was developed in [2] that ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pages 135--142, 1994.

....it is a general method to use landmarks for localization. To perform reliable navigation, artificial patterns are often used as landmarks[1] However, it is a reluctant work for us to arrange artificial landmarks. Visual features such as vertical edges of door or obstacle are used as landmarks[2, 3]. However, such features may not be stable, that is, may not be observable under various conditions of lighting and background scene changed by viewpoints[4] Therefore, it is desirable to determine stable landmarks based on observed data. 3 Proc. of IROS 99, pp. 781 786, Kyongju, Korea, Oct. ....

M. Betke and L. Gurvits, "Mobile Robot Localization Using Landmarks," IEEE Trans. on Robotics and Automation, Vol. 13, No. 2, pp. 251-263, 1997.

....last category, with a 3D environment and a calibrated camera. The existence of vertical edges in the scene supports the building of a 2D map in the first step of our algorithm. Similar 2Dlay out computations can be found in the works of Brooks [6] Kriegman [15] Kak [14] Lebegue [17] and Betke [4]. It should be mentioned here that the vertical line formulation is equivalent with using 1D projections of points (a horizontal slice of the environment) in order to recover a 2D map. Faugeras et al. 9] formulated and solved the problem of 2D recovery from 1D projection in a projective geometric ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13:251--263., 1997.

....landmarks. He developed an algorithm for performing localization from this data in time, where is the number of landmarks. Sugihara s extensions of this method yielded an algorithm for a robot with a compass and an algorithm for the case where the landmarks are distinguishable. Betke and Gurvits [28] further consider the case where the landmarks are distinguishable. By representing the landmark positions as complex numbers, they obtained a linear time algorithm with a least squares error criterion. Another localization technique uses a search tree [29] 30] to perform matching between the ....

M. Betke and L. Gurvits, "Mobile robot localization using landmarks," IEEE Trans. Robot. Automat., vol. 13, pp. 251--263, Apr. 1997.

....room. Blum, Raghavan, and Schieber [10] consider a robot navigating in an unknown two dimensional geometric terrain with convex obstacles. Bar Eli, Berman, Fiat, and Yan [4] give an efficient algorithm for reaching the center of a two dimensional room with obstacles. Betke and Gurvits [7], Kleinberg [16] and Romanik and Schuierer [22] address the problem of localizing a mobile robot in 4 its environment. Blum and Chalasani [9] consider the problem of finding a k trip shortest path in the environment. There are many other related papers in the literature (e.g. 15, 14, 20] ....

Margrit Betke and Leonid Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, April 1997.

....by itself. Automatically recognizing traffic signs in images is very valuable for mobile robot or autonomous vehicle navigation. A robot that can recognize a traffic sign as a familiar landmark in its map of the environment can then use this information to localize itself in its environment [2]. Our method stands apart from previous approaches to traffic sign recognition because first, it is efficiently applied to real world landscape images (as opposed to Ettinger s isolated signs [6] and second, it does not rely on color perception which is very sensitive to lighting changes (as ....

M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE/RSJ/GI Intl. Conf. on Intelligent Robots and Systems, 1994.

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Margrit Betke and Leonid Gurvits. Mobile Robot Localization using Landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, April 1997.

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M. BETKE AND L. GURVITS, Mobile robot localization using landmarks, in Proc. IEEE/RSJ/GI Internat. Conference on Intelligent Robots and Systems, Munich, Germany, September 1994, IEEE Computer Society Press, Los Alamitos, CA, pp. 135--142. To appear in IEEE Trans. on Robotics and Automation.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, April 1997.

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M. Betke and L. Gurvits, "Mobile robot localization using landmarks, " IEEE Transactions on Robotics and Automation, vol. 13, no. 2, pp. 251--263, 1997.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94), pages 135-142, 1994.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transanctions on Robotics and Automation, 13:251263, 1997.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, April 1997.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, 1997.

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Betke, M., Gurvits, L.: Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation 13 (1997) 251--263

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M. Betke and L. Gurvits, "Mobile robot localization using landmarks," IEEE Trans. Robot. Automat., vol. 13, pp. 251--263, Apr. 1997.

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Betke, M. and Gurvits, L. Mobile Robot Localization using Landmarks. no. SCR-94-TR-474, Siemens Corporate Research, Pinceton, December 1993. will also appear in the IEEE Transactions on Robotics and Automation.

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M. Betke and L. Gurvitis. Mobile robot localization using landmarks. In IEEE International Conference on Robotics and Automation, volume 2, pages 135--142, May 1994. 13, 41, 48

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M. Betke and L. Gurvitis, "Mobile robot localization using landmarks", in Proceedings of the IEEE International Conference on Robotics and Automation, volume 2, pages 135--142, May 1994.

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M. Betke, L. Gurvitis, Mobile robot localization using landmarks, in: IEEE International Conference on Robotics and Automation, vol. 2, 1994, pp. 135--142.

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Margrit Betke and Leonid Gurvits. Mobile Robot Localization Using Landmarks. Extended abstract, 1994. 21

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M. Betke and K. Gurvits, "Mobile robot localization using landmarks," in Proceedings of the IEEE International Conference on Robotics and Automation, 2, pp. 135--142, 1994.

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M. Betke and L. Gurvits. Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2):251--263, 1997.

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Betke, M., & Gurvits, L. (1993). Mobile robot localization usinglandmarks (Tech. rep. SCR-94-TR-474). Princeton: Siemens Corporate Research.

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M. Betke and L. Gurvits, Mobile Robot Localization using Landmarks, IEEE Transactions on Robotics and Automation, 1997.

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Betke, M. and Gurvits, L. 1997. Mobile robot localization using landmarks. IEEE Trans. on Robotics and Automation, 13(2):251--263.

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