A Comparative Study of Robot Localization Methods

Department of Robotics, Beijing Affiliated School of Beijing Normal University, Beijing, 100000, China

^* Corresponding author: zty20071018@outlook.com

Abstract

This paper compares three representative robot localization methods—odometry-based localization, particle-filter (Monte Carlo) localization, and SLAM—and analyzes their principles, strengths, and limits. Odometry provides fast motion-based estimates but suffers from drift; particle filters reduce drift through probabilistic sampling and sensor fusion; SLAM jointly estimates pose and a map and can correct long-term errors through loop closure. We present concise tables that summarize advantages, limitations, and computational trade-offs for each method and offer a cross-method comparison. We also discuss typical application scenarios for short-range low-cost robots, service robots in known maps, and long-term autonomous navigation in unknown environments. Finally, we outline a simple hybrid idea in which odometry provides the motion prior while a particle filter or SLAM module performs measurement correction. The paper emphasizes practical trade-offs among accuracy, robustness, real-time performance, and computing cost, and clarifies where each approach is most appropriate. Although related literature is abundant, our own experimental validation is planned as future work.