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.

An important problem in robotics is to determine and maintain the position of a robot that moves through a known environment with indistinguishable landmarks. This problem is made difficult due to the inherent noise in robot movement and sensor readings. Monte Carlo Localization (MCL) is a frequently used technique to solve this problem, and its performance intuitively depends on how the robot explores the environment and the position of the landmarks. In this paper, we propose a navigation policy to reduce the number of steps required by the robot to find its location together with the optimal landmark placement for this policy. This proposal is evaluated and compared against other policies using two specific metrics that indicate its superiority.

An important problem in robotics is to determine and maintain the position of a robot that moves through a previously known environment with indistinguishable reference points. This problem is made difficult due to the inherent noise in robot movement and identification of reference points and due to multiple identical reference points. Monte Carlo Localization (MCL) is a frequently used technique to solve this problem and its performance intuitively depends on how the robot explores the map. In this paper, we evaluate the performance of MCL under different navigation policies. In particular, we propose a novel navigation policy that aims in reducing the uncertainty in the robot's location by making a greedy movement at every step. We show that this navigation policy can significantly outperform random movements, particularly when the map has few reference points. Moreover, differently from random movements, the performance of the proposed navigation policy is not monotonic with the number of reference points on the map.