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SLAM performance evaluation framework
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# SLAMBench
[](https://travis-ci.org/pamela-project/slambench2)
Code has been added for the most recent paper based on SLAMBench:
[Robust SLAM Systems: Are We There Yet?](https://arxiv.org/abs/2109.13160) \
https://robustslam.github.io/evaluation
## Contents
* [What is SLAMBench?](#what-is-slambench)
* [How to set up SLAMBench?](#how-to-set-up-slambench)
* [What algorithms does SLAMBench support?](#what-algorithms-does-slambench-support)
* [How to run an existing algorithm with SLAMBench?](#how-to-run-an-existing-algorithm-with-slambench)
* [How to add a new benchmark in SLAMBench?](#how-to-add-a-new-benchmark-in-slambench)
* [Known Issues](#known-issues)
* [Release History](#release-history)
## Most frequent questions ##
### Where are the algorithms ? ###
Use the following command to list all available algorithms:
```
make usecases
```
## What is SLAMBench? ##
SLAMBench is a SLAM performance benchmark that combines a framework for quantifying quality-of-result with instrumentation of accuracy, execution time, memory usage and energy consumption. It also include a graphical interface to visualize these information.
SLAMBench offers a platform for a broad spectrum of future research in jointly exploring the design space of algorithmic and implementation-level optimisations. It targets desktop, laptop, mobile and embedded platforms. Some of the benchmarks (in particular KFusion) were tested on Ubuntu, OS X and Android (more information about android here [https://github.com/bbodin/slambench-android](https://github.com/bbodin/slambench-android)).
SLAMBench currently supports the following algorithms:
* ORB-SLAM3 [Campos et al, ARXIV'20]: C++ as distributed by https://github.com/UZ-SLAMLab
* ReFusion [Palazollo et al. IROS'19]: CUDA as distributed by https://github.com/PRBonn
* OpenVINS [Geneva et al. IROS'19]: C++ as distributed by https://github.com/rpng/
* Supereight [Vespa et al. RA-L'18]: C++, OpenMP as distributed by https://github.com/emanuelev
* BundleFusion [Dai et al. ACM TOG'17]: CUDA as distributed by https://github.com/niessner
* SemanticFusion [McCormac et al. ICRA'17]: CUDA as distributed by https://github.com/seaun163
* ORB-SLAM2 [Mur-Artal et al, TOR'15 and TOR'17]: C++ as distributed by https://github.com/raulmur
* DSO [Engel et al. Arxiv'16]: C++ as distributed by https://github.com/JakobEngel
* ElasticFusion [Whelan et al, IJRR'16]: CUDA as distributed by https://github.com/mp3guy
* InfiniTAMv2 [Kahler et al, ISMAR'15]: C++, OpenMP and CUDA versions as distributed by https://github.com/victorprad/
* KinectFusion [Newcombe et al. ISMAR'11]: C++, OpenMP, OpenCL and CUDA inspired by https://github.com/GerhardR
* LSDSLAM [Engel et al, ECCV'14]: C++, and threaded as distributed by https://github.com/tum-vision/ and modified by https://github.com/mp3guy
* MonoSLAM [Davison et al, TPAMI'07]: Original version as distributed by https://github.com/hanmekim/
* OKVIS [Leutenegger et al, IJRR'15]: Original version as distributed by https://github.com/ethz-asl
* PTAM [Klein et al, ISMAR'07 and ECCV'08]: Original version as distributed by https://github.com/Oxford-PTAM/
* SVO [Forster et al, ICRA'14]: Original version as distributed by https://github.com/uzh-rpg/rpg_svo/ (a more recent version available at http://rpg.ifi.uzh.ch/svo2.html)
**IMPORTANT: If you use any of those algorithms in scientific publications, you should refer to the respective publications.**
In addition, if you use SLAMBench in scientific publications, we would appreciate citations to the following papers:
```
@inproceedings{bujanca2021robust,
author={Bujanca, Mihai and Shi, Xuesong and Spear, Matthew and Zhao, Pengpeng and Lennox, Barry and Luj{\'a}n, Mikel},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Robust SLAM Systems: Are We There Yet?},
year={2021},
doi={10.1109/IROS51168.2021.96368Excerpt of 24,459 characters
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