Pitch Variation Filter for LiDAR-Only SLAM and Localization in Self-Balancing Mobile Robot

Abstract

This paper addresses the Pitch Variation Problem in Two-Wheeled Self-Balancing (TWSB) robots that use 2D LiDAR for Simultaneous Localization And Mapping (SLAM). The issue arises from sudden accelerations or decelerations, leading to abrupt pitch variations that cause the 2D LiDAR to capture data from unintended surfaces, such as the ground or ceiling, destabilizing the robot's position estimation. To mitigate this, we propose a novel preprocessing method that efficiently removes point clusters affected by pitch variation by leveraging their distinct characteristics, without the need for an alignment process. Experimental results demonstrate that our method reduces errors by at least 24.31% across various scan matching algorithms. Furthermore, as the proposed method operates independently of SLAM, it can be seamlessly integrated into a wide range of systems and has been shown to substantially enhance SLAM performance when used alongside existing algorithms.