R.J. Rikoski, J.J. Leonard, and Newman P.M. Stochastic mapping frameworks. In IEEE International pages 426--433, 2002.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....feature. The approach presented here considers the case when the observation consists of a range and bearing to a target implying that a feature estimate is fully observable with a single observation. For analogous methods for the case in which this is not the case refer to the work of Rikoski [30] and Deans [6] in which more temporal information is stored in the fitler. 3.1 Associating Observations When observations are received by the SLAM process, the first step is to perform data association between the observed feature and the features currently in the map. This step is one of the ....

R.J. Rikoski, J.J. Leonard, and P.M.Newman. Stochastic mapping frameworks. In Proc. IEEE Int. Conf. on Robotics and Automation, volume 1, pages 426--433, 2002.

....structure from motion in computer vision [5] By adjusting the duration of the time window of measurements, the method can be varied between the two extremes of either a strict recursive solution, using only the most recent measurements, or a full batch algorithm using all the data. Rikoski et al. [6], 7] present a related approach, known as delayed decision making (DDM) developed for SLAM using sonar data. The in air range only SLAM problem has been touched on by Kantor [8] using a Kalman filter with linearization of measurement models using some prior of knowledge of transponder ....

....course changes (a 90 degree turn for example) to resolve baseline ambiguities. A series of experiments is planned to test this approach. A related approach to this problem currently under evaluation is to use odometry (DVL) data and the Delayed Decision Making (DDM) architecture suggested in [6] to initialize transponder locations after only a small change in vehicle position. A possible benefit of this approach is being able to instantlate and remove multiple transponder location hypotheses. By doing so any cross base line ambiguity would be naturally resolved as the vehicle changes ....

R. Rikoski, J. Leonard, and P. Newman, "Stochastic map- ping frameworks," in Proc. IEEE Int. Conf. Robotics and Automation, 2002, pp. 426 433.

....ment. I. MOTIVATION This paper describes a method that enables a robot using feature based navigation techniques to autonomously explore its environment. Recently, there has been much written about feature based Simulta neous Localization and Mapping (SLAM) algorithms [1] 2] 3] 4] [5]. Such algorithms attempt to answer the question where am I and what is around me . That answered the next obvious question is where should I go next . The exploration algorithm presented in this paper provides a sensible answer to this question. In [4] an indoor mobile robot was manually di ....

....of mapped features. Feature based exploration is independent of the kind of sensing employed. Any suitable propriocep tive sensor data can be fused to form a feature based representation. The evidence for the existence of a feature can be accrued over multiple time steps and robot locations [5]. Several successful contemporary techniques [7] 8] take an opposite approach and use free space analysis such as Voronoi diagrams to decide where to go next and model free space. This however is counter to the Goal Sample Points Not visible from goal point G S2 . S1 Fz. F2 , ....

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R. Rikoski, J. Leonard, and P. Newman, "Stochastic map- ping frameworks," in Proc. IEEE Int. Conf. Robotics and Automation, 2002, pp. 426 433.

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R.J. Rikoski, J.J. Leonard, and Newman P.M. Stochastic mapping frameworks. In IEEE International pages 426--433, 2002.

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