# SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

arXiv: https://arxiv.org/abs/1808.06820 · HTML: https://mlsanity.com/paper/1808.06820/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter](https://mlsanity.com/paper/2209.06918/) (2022)
- [Greedy-Based Feature Selection for Efficient LiDAR SLAM](https://mlsanity.com/paper/2103.13090/) (2021)
- [VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets](https://mlsanity.com/paper/2504.04457/) (2025)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)
- [A Real-Time Fusion Framework for Long-term Visual Localization](https://mlsanity.com/paper/2210.09757/) (2022)

## Abstract (arXiv)

SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phonebased AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs across SLAM systems.