Self-Localization of Parking Robots Using Square-Like Landmarks
It provides a low-cost, high-accuracy solution for parking robot navigation, but it is incremental as it builds on existing particle filter and landmark-based methods.
The paper tackles self-localization for parking robots by using square-like landmarks like pillars and charging piles, achieving a positioning accuracy below 0.20 m and heading error below 1° in simulation.
In this paper, we present a framework for self-localization of parking robots in a parking lot innovatively using square-like landmarks, aiming to provide a positioning solution with low cost but high accuracy. It utilizes square structures common in parking lots such as pillars, corners or charging piles as robust landmarks and deduces the global pose of the robot in conjunction with an off-line map. The localization is performed in real-time via Particle Filter using a single line scanning LiDAR as main sensor, an odometry as secondary information sources. The system has been tested in a simulation environment built in V-REP, the result of which demonstrates its positioning accuracy below 0.20 m and a corresponding heading error below 1°.