# Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment

arXiv: https://arxiv.org/abs/2501.09490 · HTML: https://mlsanity.com/paper/2501.09490/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [Keyframe-based monocular SLAM: design, survey, and future directions](https://mlsanity.com/paper/1607.00470/) (2016)
- [Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect](https://mlsanity.com/paper/1705.04300/) (2017)
- [Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM](https://mlsanity.com/paper/2407.15305/) (2024)

## Built on by (most cited first, 2 of 2)

- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)
- [MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains](https://mlsanity.com/paper/2609.03984/) (2026)

## Abstract (arXiv)

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.