Improving Monte Carlo Localization with Strategic Navigation Policies and Optimal Landmark Placement

Abstract

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.