• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.575-582
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• 2013

This paper proposes a map building using the ICP algorithm based robot localization prediction. Proposed method predicts a robot location to dead reckoning, makes a map in the ICP algorithm. Existing method makes a map building and robot position using a sensor value of reference data and current data. In this case, a large interval of the difference of the reference data and the current data is difficult to compensate. The proposed method can map correction through practical experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.391-399
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• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.353-360
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• 2012

This paper describes a SLAM method which estimates landmark locations and robot pose simultaneously. The particle filter can deal with nonlinearity of robot motion as well as the non Gaussian property of robot motion uncertainty and sensor error. The state to be estimated includes the locations of landmarks in addition to the robot pose. In the experiment, four beacons which transmit ultrasonic signal are used as landmarks. The robot receives the ultrasonic signals from the beacons and detects the distance to them. The method uses rang scanning sensor to build geometric feature of the environment. Since robot location and heading are estimated by the particle filter, the scanned range data can be converted to the geometric map. The performance of the method is compared with that of the deadreckoning and trilateration.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.