A ROS 2 Static–Dynamic Obstacle Filtering Framework for Robust Mapping in Dynamic Environments

Abstract

This work presents a modular ROS 2 framework for separating static environmental structure from dynamic obstacles in LaserScan-based robotic mapping and navigation. The framework processes unified multimodal range observations to distinguish persistent structures from transient measurements, providing a static scan for SLAM-based map construction and a dynamic scan for real-time obstacle avoidance. In addition, dynamic observations may be tracked to estimate obstacle motion while preserving compatibility with conventional ROS 2 LaserScan interfaces. The approach is designed as a reusable preprocessing layer for mobile robots operating in dynamic indoor environments, improving map consistency while retaining moving obstacles for navigation and collision avoidance.