SAR-SLAM: Semantic-Aware Recognition for Dynamic SLAM in Robotic Applications
TL;DR
This paper introduces SAR-SLAM (Semantic-Aware Recognition SLAM), an RGB-D SLAM framework that robustly handles dynamic scenes containing moving people and objects using dual semantic geometric processing, and remains competitive with state-of-the-art dynamic SLAM methods across a range of dynamic scenarios.
Abstract
Simultaneous Localization and Mapping (SLAM) is essential for autonomous systems navigating in human-centric environments, yet conventional systems fail when people and objects move through the scene. This paper introduces SAR-SLAM (Semantic-Aware Recognition SLAM), an RGB-D SLAM framework that robustly handles dynamic scenes containing moving people and objects using dual semantic geometric processing. First, we employ YOLOv8-based semantic segmentation to identify dynamic objects and generate initial detection masks. Second, we apply RANSAC-based Homography analysis to perform geometric motion verification, distinguishing truly moving objects from stationary ones by analyzing feature correspondence patterns. Third, an adaptive fusion mechanism combines both semantic and geometric evidence while incorporating temporal consistency and coverage constraints to maintain system stability. The system is implemented as a modular ROS2 package, enabling smooth integration with robotic systems and compatibility with existing navigation frameworks. SAR-SLAM reduces Absolute Trajectory Error by up to 96% over ORB-SLAM3 on the dynamic sequences of the TUM RGB-D benchmark, and remains competitive with state-of-the-art dynamic SLAM methods across a range of dynamic scenarios.