Influence of Location and Number of Landmarks on the Monte Carlo Localization Problem - Ferreira Júnior, Henrique José dos S.; Figueiredo, Daniel Ratton (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2018: Anais do XV Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2018.4435, 2018)
An important problem in robotics is to determine and maintain the position of a robot that moves through a previously known environment with reference points that are indistinguishable, which is made difficult due to the inherent noise in robot movement and identification of reference pints. Monte Carlo Localization (MCL) is a frequently used technique to solve this problem and its performance intuitively depends on reference points. In this paper we evaluate the performance of MCL as a function of the number of reference points and their positioning in the environment. In particular, we show that performance is not monotonic in the number of reference points and that a random positioning of the reference points is close to optimal.