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SLAMBench 3.0

Copyright (c) 2014-2021 University of Edinburgh, Imperial College, University of Manchester. Developed in the PAMELA project, EPSRC Programme Grant EP/K008730/1 and

The RAIN Hub, funded by the Industrial Strategy Challenge Fund, part of the UK government’s modern Industrial Strategy. The fund is delivered by UK Research and Innovation and managed by EPSRC [EP/R026084/1].

Build Status

Contents

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).

SLAMBench currently supports the following algorithms:

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 paper:

@inproceedings{bujanca2019slambench,
  title={SLAMBench 3.0: Systematic automated reproducible evaluation of SLAM systems for robot vision challenges and scene understanding},
  author={Bujanca, Mihai and Gafton, Paul and Saeedi, Sajad and Nisbet, Andy and Bodin, Bruno and O'Boyle, Michael FP and Davison, Andrew J and Kelly, Paul HJ and Riley, Graham and Lennox, Barry and others},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={6351--6358},
  year={2019},
  organization={IEEE}
}

How to set up SLAMBench ?

As SLAMBench deals with multiple SLAM algorithms, dependencies might be difficult to install on any systems. To ease the usage of SLAMbench we provide auto-installation of dependencies and we recommend to use fresh installation of the operating systems Ubuntu 16/18 or Fedora 24/25/26/27 that are known to work fine.

Dependency installation

Required by SLAMBench framework

  • CMake 2.8.11 or higher is required.
  • Make
  • GCC C/C++
  • Boost (Optional)
  • GLUT (Optional)

Required by benchmarks dependencies

  • Git
  • Mercurial
  • wget
  • unzip
  • lapack
  • blas
  • findutils
  • cvs
  • glog
  • gflags

To install them

With Fedora 24: dnf install -y gtk2-devel vtk-devel cmake make git mercurial wget unzip gcc gcc-c++ lapack blas lapack-devel blas-devel findutils cvs glut-devel glew-devel boost-devel glog-devel gflags-devel libXmu-devel

With Ubuntu 16.10: apt-get -y install libvtk6.3 libvtk6-dev unzip libflann-dev wget mercurial git gcc cmake python-numpy freeglut3 freeglut3-dev libglew1.5 libglew1.5-dev libglu1-mesa libglu1-mesa-dev libgl1-mesa-glx libgl1-mesa-dev libxmu-dev libxi-dev libboost-all-dev cvs libgoogle-glog-dev libatlas-base-dev gfortran gtk2.0 libgtk2.0-dev libyaml-dev build-essential bison flex libyaml-cpp-dev

With Ubuntu 16.04: apt-get -y install libvtk6.2 libvtk6-dev unzip libflann-dev wget mercurial git gcc cmake python-numpy freeglut3 freeglut3-dev libglew1.5 libglew1.5-dev libglu1-mesa libglu1-mesa-dev libgl1-mesa-glx libgl1-mesa-dev libxmu-dev libxi-dev libboost-all-dev cvs libgoogle-glog-dev libatlas-base-dev gfortran gtk2.0 libgtk2.0-dev libproj9 libproj-dev libyaml-0-2 libyaml-dev libyaml-cpp-dev libhdf5-dev libhdf5-dev

With Ubuntu 14.04: apt-get -y install libvtk6-dev unzip libflann-dev wget mercurial git gcc cmake python-numpy freeglut3 freeglut3-dev libglew1.5 libglew1.5-dev libglu1-mesa libglu1-mesa-dev libgl1-mesa-glx libgl1-mesa-dev libxmu-dev libxi-dev libboost-all-dev cvs libgoogle-glog-dev libatlas-base-dev gfortran gtk2.0 libgtk2.0-dev

Special requirements for CUDA

Tu run the CUDA implementation of some of the algorithms, you will need extra dependencies.

With Ubuntu: apt-get -y install nvidia-cuda-toolkit clinfo

With Fedora: yum install cuda

Compilation of SLAMBench and its benchmarks

1. dependencies

Install dependencies first [NOTE can be installed by the user on their system as well]:

make deps

The idea is to maximise the chance of a good build, by selection the best cocktail of libraries. This will download and compile the following applications : Brisk, Ceres, CVD, Eigen3, Flann, FreeImage, G2O, Gvars, OpenCV, OpenGV, OpenTuner, Pangolin, PCL, Suitesparse, TooN.

more information is available in the framework/makefiles/README.md file.

2. SLAMBench framework

To compile SLAMBench (framework only, without any algorithms), run

make slambench

3. Usescases

To compile a specific use-case, you will need to specify identifiers :

make slambench APPS=kfusion,lsdslam

You will find more information about the identifiers and instructions to download and compile use-cases with the make usecases command.

4. Datasets

To test a SLAM algorithm you can use a Live camera, or a dataset. SLAMBench provides tools to automatically download some of the most popular datasets, that is ICL-NUIM and TUM RGB-D. The file format (*.slam) will then include all the most important information about the dataset, those are Camera calibration setting, initial position of the sensors, and the ground truth.

As an example to download and generate the Living Room Trajectory 2 from the ICLNUIM dataset, you can run the following :

> make datasets/ICL_NUIM/living_room_traj2_loop.slam

SLAMBench currently supports the following datasets:

  • OpenLORIS [Shi et al, ICRA'20]: Lifelong SLAM dataset
  • Bonn Dynamic [Palazollo et al. IROS'19]: Dynamic scene dataset
  • UZH-FPV [Delmerico et al. ICRA'19]: Drone racing dataset
  • ETH Illumination [Park et al, ICRA'17]: Illumination changes dataset
  • VolumeDeform [Innmann et al, ECCV'16]: Non-rigid reconstruction
  • EuRoC MAV [Burri et al, IJRR'16]: Micro Aerial Vehicle dataset
  • ICL-NUIM [Handa et al, ICRA'14]: Synthetic dataset
  • TUM RGB-D [Sturm et al, IROS'12]: A standard SLAM benchmark

A complete list of the dataset sequences available is provided by the command make datasets.

How to run an existing algorithm with SLAMBench ?

Once you have compile a benchmark, there are several ways to run it. For each implementation of this benchmark, you will find a specific library. As an example, with KinectFusion, after running make benchmark APPS=kfusion, you may found the following libraries in the build/lib directory :

> ls build/lib/libkfusion-*-library.so

build/lib/libkfusion-cpp-library.so   
build/lib/libkfusion-notoon-library.so      
build/lib/libkfusion-openmp-library.so
build/lib/libkfusion-cuda-library.so  
build/lib/libkfusion-opencl-library.so

We can see five different implementations (cpp, notoon, and openmp, cuda and opencl). The list of available binaries depends of the dependencies you installed beforehand. For example, you need CUDA to compile the kfusion-cuda.

Running a benchmark (e.g. KinectFusion)

To run one algorithm you will need to use a loader. There is currently two different loaders supported, benchmark and pangolin. Both loader are used the same way, except that benchmark is a command line application dedicated to measurements, while pangolin is a graphical user interface less precise in term of measurement but which provide a good interface for demonstrations.

Each loader has a series of parameters to specify such as the dataset location, or the libraries to run. The list of those parameters is available by using the "--help" parameters.

> ./build/bin/benchmark_loader --help 
 == SLAMBench Configuration ==
  Available parameters :
   -fl            --frame-limit           : last frame to compute (Default=0)
   -o             --log-file              : Output log file (Default=)
   -i             --input                 : Specify the input file or mode. (Default=)
   -load          --load-library          : Load a specific SLAM library. (Default=)
   -dse           --dse                   : Output solution space of parameters. (Default=false)
   -h             --help                  : Print the help. (Default=false)
   -nf            --negative-focal-length : negative focal length (Default=false)
   -realtime      --realtime-mode         : realtime frame loading mode (Default=false)
   -realtime-mult --realtime-multiplier   : realtime frame loading mode (Default=1)
   -fo            --file-output           : File to write slamfile containing outputs (Default=)

Then if you run the loader again, while providing a dataset file -i dataset.slam, you will see new parameters dedicated to the dataset :

> ./build/bin/benchmark_loader -i datasets/ICL_NUIM/living_room_traj2_loop.slam --help
 == SLAMBench Configuration ==
  Available parameters :
   ....
   -Camera-intrisics --Camera-intrisics       : (Default=nullptr  Current=0.751875,1,0.4992185,0.4989583)
   -Depth-intrisics  --Depth-intrisics        : (Default=nullptr  Current=0.751875,1,0.4992185,0.4989583)
   -Depth-dip        --Depth-disparity-params : (Default=nullptr  Current=0.001,0)
   -Camera-intrisics --Camera-intrisics       : (Default=nullptr  Current=0.751875,1,0.4992185,0.4989583)

Finally is you add a library name -load libname, more parameter can be seen :

> ./build/bin/benchmark_loader -i datasets/ICL_NUIM/living_room_traj2_loop.slam -load ./build/lib/libkfusion-cpp-library.so  --help
 == SLAMBench Configuration ==
  Available parameters :

    ....

   -c                --compute-size-ratio     : Compute ratio (Default=1)
   -r                --integration-rate       : integration-rate  (Default=2)
   -t                --tracking-rate          : tracking-rate     (Default=1)
   -z                --rendering-rate         : rendering-rate    (Default=4)
   -l                --icp-threshold          : icp-threshold     (Default=1e-05)
   -m                --mu                     : mu                (Default=0.1)
   -s                --volume-size            : volume-size       (Default=8,8,8)
   -d                --volume-direction       : volume-direction  (Default=4,4,4)
   -v                --volume-resolution      : volume-resolution (Default=256,256,256)
   -y1               --pyramid-level1         : pyramid-level1    (Default=10)
   -y2               --pyramid-level2         : pyramid-level2    (Default=5)
   -y3               --pyramid-level3         : pyramid-level3    (Default=4)

You can run a loader with only one dataset at a time and it must be specified first.

In the next section we will explain how to use SLAMBench to evaluate the performance of a SLAM algorithm.

Evaluating a benchmark (eg. KinectFusion)

SLAMBench works with Metrics and Outputs elements. When you run the benchmark_loader or the pangolin_loader these are those elements that you can visualize. Metrics are components generated by SLAMbench framework really, while Outputs are generated by the algorithm or may be elements post-processed by SLAMbench (such as the aligned trajectory with the ground truth).

Les us run the benchmark loader. Its output is composed of two main parts, the Properties section, and the Statistics section. the properties section details all the parameters used for the experiment (could been changed or not via the command line). the statistics section repart all the outputs and metrics selection for output in the benchmark loader.

> ./build/bin/benchmark_loader -i datasets/ICL_NUIM/living_room_traj2_loop.slam -load ./build/lib/libkfusion-cpp-library.so 

SLAMBench Report run started:	2018-02-02 04:41:31

Properties:
=================

frame-limit: 0
log-file: 
input: datasets/ICL_NUIM/living_room_traj2_loop.slam
load-library: ./build/lib/libkfusion-cpp-library.so
dse: false
help: false
negative-focal-length: false
realtime-mode: false
realtime-multiplier: 1
file-output: 
Camera-intrisics: 0.751875,1,0.4992185,0.4989583
Depth-intrisics: 0.751875,1,0.4992185,0.4989583
Depth-disparity-params: 0.001,0
Camera-intrisics: 0.751875,1,0.4992185,0.4989583
compute-size-ratio: 1
integration-rate: 2
tracking-rate: 1
rendering-rate: 4
icp-threshold: 1e-05
mu: 0.1
volume-size: 8,8,8
volume-direction: 4,4,4
volume-resolution: 256,256,256
pyramid-level1: 10
pyramid-level2: 5
pyramid-level3: 4
Statistics:
=================

Frame Number	Timestamp	Duration_Frame	GPU_Memory	CPU_Memory		Duration_Preprocessing	Duration_Tracking	Duration_Integration	Duration_Raycasting	Duration_Render	X	Y	ZATE_Frame
1	0.0000000000	0.7679200000	0	623801799		0.1254800000	0.0195420000	0.0561620000	0.0000030000	0.5667170000	4.0000000000	4.0000000000	4.0000000000	0.0000002980
2	1.0000000000	0.2003970000	0	623801799		0.1242030000	0.0156470000	0.0581670000	0.0000000000	0.0023710000	4.0000000000	4.0000000000	4.0000000000	0.0010031639
3	2.0000000000	0.1989980000	0	623801799		0.1233680000	0.0152360000	0.0580180000	0.0000000000	0.0023690000	4.0000000000	4.0000000000	4.0000000000	0.0055015362
4	3.0000000000	0.7518580000	0	623801799		0.1220660000	0.0152080000	0.0563070000	0.5559520000	0.0023170000	4.0000000000	4.0000000000	4.0000000000	0.0036504765
5	4.0000000000	1.3683420000	0	623801799		0.1240890000	0.0767240000	0.0581630000	0.5504240000	0.5589330000	3.9957129955	4.0020360947	4.0009112358	0.0021276891
...

How to add a new benchmark in SLAMBench ?

The main reason to provide a new version of SLAMBench is not only because of the introduction of new benchmarks but also because we provide now a clear and specific API for SLAM algorithms to be implemented in order to add a new algorithm.

void sb_new_slam_configuration(SLAMBenchConfiguration ** slam_settings) ;
bool sb_init_slam_system(SLAMBenchConfiguration * slam_settings) ;
bool sb_update_frame (Sensor * type) ;
bool sb_process_once () ;
bool sb_clean_slam_system();
bool sb_update_outputs(SLAMBenchUI * );

If each of those functions are correctly implemented for a specific implementation of a specific algorithm, then this algorithm is compatible with SLAMBench and ben be evaluated as well.

In this section we will present those functions one by one.

bool sb_new_slam_configuration(SLAMBenchLibraryHelper * slam_settings)

This function is called first, and only once, SLAM systems is expected to provide its parameters.

Example :


 bool sb_new_slam_configuration(SLAMBenchLibraryHelper * slam_settings)  {
     slam_settings->addParameter(TypedParameter<float>("c", "confidence",     "Confidence",      &confidence, &default_confidence));
     slam_settings->addParameter(TypedParameter<float>("d", "depth",          "Depth",           &depth,      &default_depth));
     slam_settings->addParameter(TypedParameter<int>  ("td", "textureDim",      "textureDim",       &textureDim,      &default_textureDim));
     return true;
 }

should always return true or an exception will be raised.

bool sb_init_slam_system(SLAMBenchLibraryHelper * slam_settings)

This function is called second, and only once, SLAM systems is expected to allocate memory, retrieve sensor informations.

To retrieve sensor there is SensorFinder :

	slambench::io::CameraSensorFinder sensor_finder;
	auto rgb_sensor = sensor_finder.FindOne(slam_settings->get_sensors(), {{"camera_type", "rgb"}});

SLAM systems are also expected to define there output, there is one mandatory output, the pose:

pose_output = new slambench::outputs::Output("Pose", slambench::values::VT_POSE, true);
slam_settings->GetOutputManager().RegisterOutput(pose_output);
should always return ``true`` or an exception will be raised.

bool sb_update_frame (SLAMBenchLibraryHelper * slam_settings, slambench::io::SLAMFrame* s)

The frame s is the next frame to process in the dataset (ordered by timestamps).

When sb_update_frame return false, then sb_update_frame will be directly call again with the next frame, if it return true````, then sb_process_once``` will be called once.

bool sb_process_once (SLAMBenchLibraryHelper * slam_settings)

should always return true or an exception will be raised.

bool sb_clean_slam_system()

This function is called last, and only once, SLAM systems is expected to clean everything (free memory).

 bool sb_clean_slam_system() {
     delete eFusion;
     delete inputRGB;
     delete inputDepth;
     return true;
 }

should always return true or an exception will be raised.

bool sb_update_outputs(SLAMBenchLibraryHelper * slam_settings, const slambench::TimeStamp *timestamp)

The algorithm will return visible outputs (Pose, Point cloud, Frames) as defined by the sb_init_slam_system function.

Example :


 bool sb_update_outputs(SLAMBenchLibraryHelper *lib, const slambench::TimeStamp *ts_p) {
		slambench::TimeStamp ts = *ts_p;

		if(pose_output->IsActive()) {
			// Get the current pose as an eigen matrix
			Eigen::Matrix4f mat = eFusion->getCurrPose();

			std::lock_guard<FastLock> lock (lib->GetOutputManager().GetLock());
			pose_output->AddPoint(ts, new slambench::values::PoseValue(mat));
		}

should always return true or an exception will be raised.

Know issues

Know issue with CUDA

Applications using CUDA require GCC 4.9 to work. To specify a new gcc compiler for CUDA only, you can use the CUDA_HOST_COMPILER flag as follows :

make slambench APPS=kfusion CUDA_HOST_COMPILER=/usr/local/gcc-4.9/bin/c++

Modern O.S. are now using more recent version of this compiler, this introduce several compatibility issues. To fix one of them, in the compilation process, when compiling CUDA application we use the -D_GLIBCXX_USE_CXX11_ABI=0 flag.

TODO List

  • Add office room dataset

Release history

Version 2.0 (Feb 2018)

  • This release is a complete new version

Release candidate 1.1 (17 Mar 2015)

  • Bugfix : Move bilateralFilterKernel from preprocessing to tracking
  • Bugfix : Wrong interpretation of ICP Threshold parameter.
  • Esthetic : Uniformisation of HalfSampleRobustImage kernel
  • Performance : Change float3 to float4 for the rendering kernels (No effect on OpenCL, but high performance improvement with CUDA)
  • Performance : Add a dedicated buffer for the OpenCL rendering
  • Feature : Add OSX support

Release candidate 1.0 (12 Nov 2014)

  • First public release