# Benchmarking and Comparing Popular Visual SLAM Algorithms

arXiv: https://arxiv.org/abs/1811.09895 · HTML: https://mlsanity.com/paper/1811.09895/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Comparison of modern open-source visual SLAM approaches](https://mlsanity.com/paper/2108.01654/) (2021)
- [From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations](https://mlsanity.com/paper/2502.12303/) (2025)
- [STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM](https://mlsanity.com/paper/2503.21425/) (2025)
- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)

## Abstract (arXiv)

This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset selected has a large set of image sequences from a Microsoft Kinect RGB-D sensor with highly accurate and time-synchronized ground truth poses from a motion capture system. The test sequences selected depict a variety of problems and camera motions faced by Simultaneous Localization and Mapping (SLAM) algorithms for the purpose of testing the robustness of the algorithms in different situations. The evaluation metrics used for the comparison are Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis involves comparing the Root Mean Square Error (RMSE) of the two metrics and the processing time for each algorithm. This paper serves as an important aid in the selection of SLAM algorithm for different scenes and camera motions. The analysis helps to realize the limitations of both SLAM methods. This paper also points out some underlying flaws in the used evaluation metrics.