To navigate successfully, a mobile robot must be able to estimate the spatial relationships of the objects of interest in its environment accurately. The main advantage of a bearing-only Simultaneous Localization and Mapping (SLAM) system is that it requires only a cheap vision sensor to enable a mobile robot to gain knowledge of its environment and navigate. In this paper, we focus on the representation of the spatial uncertainty of landmarks caused by sensor noise. We follow a principled approach for computing the Probability Density Functions (PDFs) of landmark positions when an initial observation is made. We characterize the PDF p(r, α) of a landmark position expressed in polar coordinates when r and α are independent, and the marginal probability p(r) of the PDF is constrained to be uniform. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Huang, H., Maire, F., & Keeratipranon, N. (2007). An improved probability density function for representing landmark positions in bearing-only SLAM systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4830 LNAI, pp. 682–686). Springer Verlag. https://doi.org/10.1007/978-3-540-76928-6_75
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