Self-Localization of Home Robot ApriAttenda TM Based on Monte Carlo Approach

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1 Self-Localization of Home Robot ApriAttenda TM Based on Monte Carlo Approach Naris Ahmad 1, Jiang Zhu I, Hideichi Nakamoto 2, and Nobuto Matsuhira 2 1 Tokyo Institute of Technology, Tokyo , Japan {ahmadn,zhujiang}@mep.titech.ac.jp 2 Toshiba Corporations, Kawasaki, Japan {hideichi.nakamoto,nobuto.matsuhira}@toshiba.co.jp Abstract. A self-localization technique for home robot is developed for in-door environment. This simple and robust self-localization approach based on Monte Carlo algorithm recovers the robot from catastrophic position tracking failure or kidnapped condition. Position of the robot at different location in the map are determined by using information obtained by laser range finder attached in front of the robot and marker distance measured by stereo vision camera. Key words: Home Robot, Self-localization, MCL 1 Introduction Home robot, like ApriAttendaTMshould have the capability to accomplish useful tasks without human intervention in indoor environments. It should be able to support housekeeping task, support elderly and handy capped people in their daily life. It should also have the ability to act as a guard, provide security and handling different home appliances [1]. For this reason mobile robot must be able to determine its position and orientation in the environment using its sensors. Any mobile robot such as home robot used in this research work that is expected to navigate in a known environment in a goal-directed and efficient manner must have the ability to self-localize, i.e. to determine its own position and orientation in the specific environment. Currently works on Simultaneous Localization and Mapping (SLAM) has gained high interest from the research community. SLAM, which uses a lot of computer resources, is suitable for unknown environment to localize the robot as well as to build the map of the environment. However for small known indoor environment i.e. the environment of home robot, an efficient and robust self-localization technique is of high importance to recover from catastrophic localization failure or incremental odometry error. Main localization strategies currently used by the research community are Kalman filter based localization, Markov localization algorithm, Monte Carlo Localization (MCL). There are some works based on combinations of two different approaches [2], [6] to get the benefit of these approaches. Until now there Copyright held by author

2 Self-Localization of Home Robot ApfiAttenda Based on Monte Carlo Approach 213 are many successful implementation of these localization algorithms for different types of robots [3]. However the work on home robot is very limited. In this work we have developed a simple MCL based approach to recover the robot from catastrophic position tracking failure or kidnapped condition. Though home robot is equipped with sonar sensors, Laser Range Finder (LRF) and camera, in the proposed approach information of LRF scan and image captured by camera are used for localization of the home robot. 2 Home Robot ApriAttenda TM Like industrial robots or special purpose robot, there is a new trend to develop personal robots and home robots, such as pet robots, partner robots, cleaner robot and security robots [1]. Toshiba Corporation is developing the home robot ApriAttenda TM (Advanced Personal Robotic Interface Type Attenda) which will communicate with people and move in the environment where people live their lives to give various services like controlling various home apparatus, provide security and information services. Fig. 1 shows the current version of the ApriAttenda TM and its parts. The robot is 450mm in diameter, 900ram in height and 60kg of weight. The robot is equipped with voice communication capability, which offers a friendly human machine interface system. It can communicate with voice as well as receive instructions from the user via Internet. It uses 16 sonar sensors to prevent collision with other object. A laser range finder is set at the front side, which can scan from -135 to 135 and detect obstacle within 20ram to 4095mm. The stereo vision system of the robot can detect markers placed on the wall and measure distances from these markers using its image processing algorithm. 3 Robot Localization Localization is one of the most difficult problems of mobile robots, specially for home robot. The knowledge of the robot position is useful to perform different tasks in home environment. 3.1 Different Localization Problems In case of the most simple localization problem, position tracking the initial robot position is known, and the problem is to compensate incremental errors in a robot's odometry. Algorithms for position tracking often make restrictive assumptions on the size of the error and the shape of the robot's uncertainty. Another important and more difficult task is the global localization problem, when a robot has to determine its position from the scratch i.e. it does not know its initial pose. In case of global localization problem the error in the robot's estimation cannot be assumed to be small. Consequently, a robot should be able to handle multiple, distinct hypotheses. A more difficult task is the kidnapped

3 214 Naris Ahmad et al. els e finder ors Fig. 1. Sensors for localization of ApriAttenda TM developed by Toshiba Corporation. robot problem, in which a well-localized robot is moved to some other place without robot's knowledge. In this case the robot might firmly believe itself to be somewhere else at the time of the kidnapping. The kidnapped robot problem is often used to test a robot's ability to recover from catastrophic localization failures. Finally, all these problems are particularly hard in dynamic environments, e.g., if robots operate in the proximity of people who corrupt the robot's sensor measurements. 3.2 Localization Problems of ApriAttenda TM Fig. 2 shows the localization error occurred in case of ApriAttenda TM. In Toshiba Science Museum it started from one specific point (actual start/end point in Fig. 2), moved around in the museum and returned to the original position. Though end position and starting position are same, the odometry reading of the robot shows that they are two different places in the map and outside the map. According to odometry reading the robot also moved to some inaccessible places, which are not possible in actual case. After moving about 30m distance in the museum, the odometry data is different from the actual position data (two axial and one angular position) almost by 10%. 3.3 Algorithms for Localization Most of the algorithms address only the position tracking problem. For small incremental errors algorithms such as Kalman filters can be used. Kalman ill-

4 Self-Localization of Home Robot ApfiAttenda Based on Monte Carlo Approach Fig. 2. Localization error of ApriAttendaTMin Toshiba Science Museum. ters estimate posterior distributions of robot poses conditioned on sensor data. However assumptions such as Gaussian noise and Gaussian distributed initial uncertainty makes Kalman filters inapplicable to global localization problems. Markov localization, another useful localization algorithm, can represent arbitrary complex probability densities at fine resolutions. However computational burden and memory requirement is very high for this approach. MCL can overcome these difficulties and very suitable for solving the global localization and kidnapped robot problem in a robust and efficient way. It can accommodate arbitrary noise distributions (and nonlinearities in robot motion and perception) and can avoid the need to extract features from the sensor data. 3.4 Monte Carlo Localization MCL represents the belief i.e. position of the robot by a set of samples or particles, drawn according to the posterior distribution over robot poses. In other words, rather than approximating posteriors in parametric form, as is the case for Kalman filter and Markov localization algorithms, MCL represents the posteriors by a random collection of weighted particles which approximates the desired distribution. Within the context of localization, the particle representation has a range of characteristics that sets it aside from previous approaches. MCL can accommodate arbitrary sensor characteristics, motion dynamics, and noise distributions. MCL also focuses computational resources in areas that are most relevant, by sampling in proportion to the posterior likelihood. MCL is based on Bayes filter, which assumes that future data is independent of past data given the knowledge of current state-an assumption typically known as the

5 216 Naris Ahmad et al. Markov assumption. MCL uses Bayes filter to estimate the posterior distribution of robot poses based on sensor data in a recursive manner. Bayes filters address the problem of estimating the state of a dynamical system from sensor measurements. For example, in mobile robot localization the dynamical system is a mobile robot and its environment, the state is the robot's pose therein (in our case is specified by a position in a two dimensional Cartesian space and the robot's heading direction), and measurements may include range measurements, camera images, and odometry readings. Bayes filters assume that the environment is Markov, that is, past and future data are (conditionally) independent if we know the current state. The main idea of Bayes filtering is to estimate a probability density over the state space conditioned on the data. For mobile robots, there are two types of data: perceptual or observation data such as data from laser range finder, sonar or camera, and odometry or action data, which carry information about robot motion. If at-1 to refer to the odometry reading that measures the motion that occurred in the time interval It-l; t], to illustrate that the motion is the result of the control action asserted at time t-1. The probability density of robot position will be p (X) : p (Xt[Ot, at_l, Or_l, at-2,..., Oo) (1) The initial belief characterizes the initial knowledge about the system state. In the absence of such knowledge, it is typically initialized by a uniform distribution over the state space. In mobile robot localization, a uniform initial distribution corresponds to the global localization problem, where the initial robot pose is unknown. As explained in [3] the recursive update equation is p (x) -~ p (ot[xt)/p (xt[xt-1, at-i) dxt-i (2) where ~ is the normalizing constant Thus robot can determine the present state without knowing details of all the previous steps if it has three kind of information. 1. State of the robot at previous step 2. Action or movement after the previous step and 3. Observation or measurement information at the present state Together with the initial belief, it defines a recnrsive estimator for the state of a partially observable system. This equation is the basis for MCL algorithms used in this work. To implement Eq.(2), two conditional densities are necessary: the probability p(xt [Xt--1, at-l), which is referred as next state density or simply motion model, and the density p(ot[xt), which is called as perceptual model or sensor model. Both of these two models do not depend on the specific time t. By simplifying the notation of these models p(x'lx, a), and p(o[x), respectively. For the perceptual model or measurement model, p(olx), mobile robots commonly uses range finders, such as ultrasonic transducer (sonar sensor), laser range finder, camera image. As mentioned earlier in ApriAttenda TM, laser range

6 Self-Localization of Home Robot ApriAttenda Based on Monte Carlo Approach 217 finder and camera are used for localization purpose in this work. In this work the problem of computing p(olx ) is decomposed into: computation of mean value and standard deviation of noise free LRF scan would generate, determination of the marker distance and integration of these three information for each position into a single density value (3). Fig. 3. Mean, Standard deviation of LRF scan line length and marker distance are used to differentiate robot position in the map and localize the robot. 4 Experimental Detail This work is intended to combine with a map building algorithm or to provide a map for starting the localization process. The proposed MCL based approach is executed in Matlab TM environment. Odometry data without localization is taken after the robot moved in the Toshiba Science Museum as shown in Fig. 2. The 2D map used in this work is similar to the actual inside environment of the museum. It is important to note that the MCL works better when the map is more complex because simple map has many similar places, which are difficult to differentiate by LRF data. In the actual map there are few similar places and the LRF scan data is also different for different places. So it mean and standard deviation will also vary significantly at different places and localization is also works better in such situation. If there are many similar places in the map, localization is more difficult. For localization the robot first starts either steps for position tracking or steps for kidnapped condition recovery. If the ideal probability (calculated using LRF scan mean, standard deviation and the distance from the specific marker using map) and from that of actual (LRF and marker information collected by robot) differs significantly (more than 20% in this work) the robot will start kidnapped recovery steps other wise it will continue position tracking. The process starts with collecting information of the odometry, LRF scan and marker distance. Then it checks which step it will start next depending

7 218 Naris Ahmad et al :.' ~ Marker :.... "~ " RoSot: ": " "'- ' O t i i i X(r~) Fig. 4. Random particles in the predefined Map. As Robot cannot move very close to the boundary particles are not distributed in area close to boundary. on the available information. As mentioned earlier if the LRF using map at position according to the odometry differs significantly from the actual LRF scan properties like mean of all scan and standard deviation it will start kidnapped recovery steps. Otherwise it will continue position tracking. In case of kidnapped recovery situation the robot distributes some predefined number of particles [100 in the example used here] in the map randomly The actual map and the map for particle distribution are different in such a manner that no particles will be distributed in the area where the robot cannot move. In this work map is defined in such a way that robot cannot move very close to the wall or other obstacle. Area closer than the radius of the robot body is not used to distribute particles for efficient use of resources. In this simulation work scan lines, which could reflect from wall or obstacles properly are considered. For this reason scan lines outside the range of 20ram to 4095mm are excluded. In this case the LRF scan data is considered reliable for determining the robot position in the map. After random distribution of particles the robot determines the ideal LRF scan mean and standard deviation at each location of the particles. It also determines the marker position according to the map. Then it starts comparing the mean according to the actual LRF scan data and the LRF scan data the determined by using the map. The closer the mean of a particle to the actual condition the higher the chance that particle will survive to the next stage of the calculation (Fig. 5). If for a particle, mean of the LRF scan data determined by map is very different from the mean of LRF scan the robot got from the actual situation, the particles has low chance to survive. Those particles with higher probability or close match with the actual scan will have more representation or more in number in the next stage(fig. 6).

8 Self-Localization of Home Robot ApnAttenda Based on Monte Carlo Approach ~ ~ ~OO 0 0 Fig. 5. Frequency of the particles after comparing the mean of LRF scan line obtained two different ways: actual scan data obtained by the robot and ideal scan line using the particle position and predefined map. 7O O 30 2O 6000 I two Fig. 6. Frequency of the particles after comparing the marker distance, mean, standard deviation of LRF scan line obtained two different ways: actual scan data obtained by the robot and ideal scan line using the particle position and predefined map.

9 220 Naris Ahmad et al. 5 Result And Discussion In this work the robot determined its position in both position tracking and kidnapped condition within 100mm of the actual position. If we want to increase accuracy of the position it will take more time by repeating the same steps several times. The calculation for the two cases is completed with in 20sec and 90sec respectively. Compared to previous works [2], [3] and [3] number of sample used in this work is very low. Matlab codes are used in this case. Computation time may improve if C/C++ or Java codes are used because Matlab itself uses a lot of the computer resources. Independent executable program obtained by compiling the Matlab codes may reduced the processing time for localization. 6 Conclusion and Future Work In this work a new approach for localizing the home robot is developed to recover it from position tracking failure and kidnapped condition using laser range finder data and marker distance. The proposed approach will be able to handle dynamic environment if the obstacle does not influence the LRF scan data significantly. Future works can be conducted to check the actual performance online. Acknowledgements This work is carried out as a 100-day-project of COE program in Tokyo Institute of Technology collaborated with Toshiba Corporation. References 1. Yoshimi, T., Matsuhira, N., Suzuki, K. and Yamamoto, D.: Development of a Concept Model of a Robotic Information Home Appliance, ApriAlpha, Proc. Of 2004 IEEE/RSJ Int. Conf. On Intelligent Robots and Sys., Sendai, Japan (2004) Thrun, S., Fox, D., Burgard, W. and Dellaert, F.: Robust Monte Carlo Localization for mobile robots, Artificial Intelligence (2001) Dellaert, F., Fox, D., Burgaxd, W. and Thrun, S.: Monte Carlo Localization for Mobile Robot, Proc. Of the 1999 IEEE Int. Conf. On Robotics & Automation, Detroit, Michigan (1999) Dissanayake, G., Durrant, H., Bailey, T.: A Computationally Efficient Solution to the Simultaneous Localization and Map Building (SLAM) Problem, Working notes of ICRA'2000 Workshop: Mobile Robot Navigation and Mapping (2000) 5. Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problem, ASME, Journal of Basic Engineering 82 (1960) Wolf, D. F., Sukhatme, G. S.: Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments, Autonomous Robotics 19 (2005) 53-65