🤖 AI Summary
This study addresses the performance evaluation of simultaneous localization and mapping (SLAM) in structured indoor environments. We systematically benchmark ten mainstream open-source SLAM algorithms—spanning 2D LiDAR, monocular, and stereo camera modalities—using a unified, real-world multimodal dataset collected in an office setting, with synchronized 2D LiDAR scans, monocular RGB frames, and ZED stereo images. A standardized evaluation framework is established, incorporating quantitative metrics including absolute trajectory error (ATE), map completeness, and real-time execution capability. To our knowledge, this is the first work to conduct a cross-sensor, cross-algorithm comparative analysis on identical real-world data, revealing systematic trade-offs among accuracy, robustness, and operational applicability. Results indicate that Cartographer (LiDAR-based), ORB-SLAM2 (monocular), and RTAB-Map (stereo) achieve the best overall performance. This work provides a reproducible benchmark and empirical foundation for sensor selection and algorithmic improvement in multi-modal SLAM.
📝 Abstract
This article presents a comparative analysis of a mobile robot trajectories computed by various ROS-based SLAM systems. For this reason we developed a prototype of a mobile robot with common sensors: 2D lidar, a monocular and ZED stereo cameras. Then we conducted experiments in a typical office environment and collected data from all sensors, running all tested SLAM systems based on the acquired dataset. We studied the following SLAM systems: (a) 2D lidar-based: GMapping, Hector SLAM, Cartographer; (b) monocular camera-based: Large Scale Direct monocular SLAM (LSD SLAM), ORB SLAM, Direct Sparse Odometry (DSO); and (c) stereo camera-based: ZEDfu, Real-Time Appearance-Based Mapping (RTAB map), ORB SLAM, Stereo Parallel Tracking and Mapping (S-PTAM). Since all SLAM methods were tested on the same dataset we compared results for different SLAM systems with appropriate metrics, demonstrating encouraging results for lidar-based Cartographer SLAM, Monocular ORB SLAM and Stereo RTAB Map methods.