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SLAM_table.tex
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SLAM_table.tex
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\documentclass[a4paper,12pt]{scrartcl}
\usepackage{url}
\usepackage{longtable} %% make tables look nice
\usepackage{multirow} %% for \multirow and \multicolumn commands
\usepackage{hyperref}
%% Bibliography-----------------------------------
\usepackage[sorting=none, backend=bibtex8]{biblatex}
\usepackage{csquotes}
\addbibresource{refs.bib}
\begin{document}
{\footnotesize
\begin{longtable}{l|l|l|l|l}
\caption{List of SLAM / VO algorithms}\\[2mm]
\label{tab:list_found_slam_algorithms}
\textbf{Name} & \textbf{Refs} & \textbf{Code} & \textbf{Sensors} & \textbf{Notes}\\
\hline
& & & &\\
\textbf{AprilSLAM} & \cite{Wang2016} (2016) & \href{https://github.com/ProjectArtemis/aprilslam}{Link} & Monocular & Uses 2D planar markers\\
& \cite{Olson2011} (2011) & & &\\
& & & &\\
\textbf{ARM SLAM} & \cite{Klingensmith2016} (2016) & - & RGB-D & Estimation of robot joint angles\\
& & & &\\
\textbf{BatSLAM} & \cite{Steckel2015} (2015) & - & Sonar & Uses RatSLAM as back-end\\
& \cite{Steckel2013} (2013) & & &\\
& & & &\\
\textbf{BundleFusion} & \cite{Dai2017} (2011) & {\href{https://github.com/niessner/BundleFusion}{Link}} & RGB-D & Focus on 3D-scanning\\
& & & &\\
\textbf{CD SLAM} & \cite{Pirker2011} (2011) & - & Monocular & Focus on dynamic environments\\
& \cite{Pirker2010} (2010) & & & Custom descriptor\\
& & & &\\
\textbf{C-KLAM} & \cite{Nerurkar2014} (2014) & - & Monocular, & Usage of inter-keyframe information\\
& & & IMU &\\
& & & &\\
\textbf{CNN SLAM} & \cite{Tateno2017} (2017) & - & Monocular & Depth prediction via CNN\\
& & & &\\
\textbf{COP SLAM} & \cite{Dubbelman2015} (2015) & - & - (back-end) & Sparse pose-graph\\
& \cite{Dubbelman2013} (2013) & & & Scale drift aware (Lie groups)\\
& \cite{Dubbelman2010} (2010) & & &\\
& & & &\\
\textbf{CoSLAM} & \cite{Zou2013} (2013) & {\href{https://github.com/danping/CoSLAM}{Link}} & Multiple cameras & Dynamic environments\\
& & & &\\
\textbf{DEMO} & \cite{Zhang2014} (2014) & - & Monocular, & Usage of depth in odometry\\
& & & RGB-D, &\\
& & & LIDAR &\\
& & & &\\
\textbf{DolphinSLAM} & \cite{Zaffari2016} (2016) & {\href{https://github.com/dolphin-slam}{Link}} & Monocular, IMU & Underwater (RatSLAM back-end)\\
& \cite{Silveira2015} (2015) & & Sonar, DVL & ROS implementation\\
& & & &\\
\textbf{DP SLAM} & \cite{Eliazar2004} (2004) & {\href{https://users.cs.duke.edu/~parr/dpslam}{Link}} & LIDAR & Particle filter back-end\\
& \cite{Eliazar2003} (2003) & & &\\
& & & &\\
\textbf{DPPTAM} & \cite{Concha2015b} (2015) & {\href{https://github.com/alejocb/dpptam}{Link}} & Monocular & Dense, estimates planar areas\\
& & & &\\
\textbf{DSO} & \cite{Engel2016} (2016) & {\href{https://github.com/JakobEngel/dso}{Link}} & Monocular & Semi-dense odometry\\
& & & & Estimates camera parameters\\
& & & &\\
\textbf{DT SLAM} & \cite{Daniel2014} (2014) & {\href{https://github.com/plumonito/dtslam}{Link}} & Monocular & Tracks 2D and 3D features (indirect)\\
& & & & Creates combinable submaps\\
& & & & Can track pure rotation\\
& & & &\\
\textbf{DTAM} & \cite{Newcombe2011} (2011) & {\href{https://github.com/anuranbaka/OpenDTAM}{Link}} & Monocular & Dense, GPU reliant\\
& & & & Robust to rapid motion\\
& & & &\\
\textbf{DVO} & \cite{Kerl2013} (2013) & {\href{https://github.com/tum-vision/dvo_slam}{Link}} & RGB-D & Entropy based method for loops\\
& & & &\\
\textbf{EIF SLAM} & \cite{Samsuri2015} (2015) & - & - (back-end) &\\
& \cite{Sola2014} (2014) & & &\\
& \cite{Kurt-Yavuz2012} (2012) & & &\\
& \cite{He2011} (2011) & & &\\
& \cite{AuatCheein2011} (2011) & & &\\
& \cite{Zhou2008} (2008) & & &\\
& & & &\\
\textbf{EKF SLAM} & \cite{Paz2008} (2008) & - & - (back-end) &\\
& \cite{Bailey2006} (2006) & & &\\
& \cite{Bailey2006a} (2006) & & &\\
& \cite{Riisgaard2004} (2004) & & &\\
& \cite{Thrun1999} (2002) & & &\\
& & & &\\
\textbf{ElasticFusion} & \cite{Whelan2015} (2015) & {\href{https://github.com/mp3guy/ElasticFusion}{Link}} & RGB-D & Windowed surfel-based fusion\\
& & & &\\
\textbf{FAB-MAP} & \cite{Glover2012} (2012) & {\href{https://github.com/arrenglover/openfabmap}{Link}} & - (back-end) & Appearance-based loop\\
& \cite{Glover2010} (2010) & & & closure detection\\
& \cite{Paul2010} (2010) & & &\\
& \cite{Cummins2009} (2009) & & &\\
& \cite{Cummins2008} (2008) & & &\\
& & & &\\
\textbf{FastSLAM} & \cite{Abouzahir2014} (2014) & {\href{https://github.com/bushuhui/fastslam}{Link}} & - (back-end) &\\
& \cite{Naminski2013} (2013) & & &\\
& \cite{Kurt-Yavuz2012} (2012) & & &\\
& \cite{Thrun2004} (2004) & & &\\
& \cite{Montemerlo2003} (2003) & & &\\
& \cite{Montemerlo2002} (2002) & & &\\
& & & &\\
\textbf{FrameSLAM} & \cite{Konolige2008} (2008) & - & Stereo & CenSure features\\
& & & &\\
\textbf{GDVO} & \cite{Zhu2017} (2017) & {\href{https://github.com/syywh/gdvo}{Link}} & Stereo & Dense\\
& & & & Dual Jacobian scheme\\
& & & &\\
\textbf{GPSLAM} & \cite{Pirker2011a} (2011) & - & RGB-D & Sparse map, dense occupancy grid\\
& & & &\\
\textbf{GP-SLAM} & \cite{Yan2017} (2017) & {\href{https://github.com/gtrll/gpslam}{Link}} & & Sparse gaussian process regression\\
& \cite{Dong2017} (2017) & & & for Lie groups\\
& & & &\\
\textbf{Graph SLAM} & \cite{Grisetti2010} (2010) & - & - (back-end) &\\
& \cite{Olson2006} (2006) & & &\\
& \cite{Thrun2006} (2006) & & &\\
& & & &\\
\textbf{Hector SLAM} & \cite{Kohlbrecher2011} (2011) & {\href{https://github.com/tu-darmstadt-ros-pkg/hector_slam}{Link}} & LIDAR, & ROS implementation\\
& & & IMU & No loop detection\\
& & & &\\
\textbf{KinectFusion} & \cite{Pirovano2012} (2012) & {\href{https://github.com/PointCloudLibrary/pcl}{Link}} & RGB-D & Object segmentation\\
& \cite{Izadi2011} (2011) & & & Uses only depth sensor\\
& \cite{Newcombe2011a} (2011) & & & GPU reliant\\
& & & &\\
\textbf{Kintinious} & \cite{Whelan2013a} (2013) & {\href{https://github.com/mp3guy/Kintinuous}{Link}} & RGB-D & Extension of KinectFusion\\
& \cite{Whelan2013} (2013) & & &\\
& \cite{Whelan2012} (2012) & & &\\
& & & &\\
\textbf{LOAM} & \cite{Zhang2015a} (2015) & {\href{https://github.com/daobilige-su/loam_continuous}{Link}} & LIDAR &\\
& & & &\\
\textbf{LSD SLAM} & \cite{Engel2015} (2015) & {\href{https://github.com/tum-vision/lsd_slam}{Link}} & Monocular, & Semi-dense\\
& \cite{Engel2014} (2014) & & Stereo & Runs on CPU\\
& \cite{Engel2013} (2013) & & &\\
& & & &\\
\textbf{MonoSLAM} & \cite{Russo2014} (2014) & {\href{https://github.com/rrg-polito/mono-slam}{Link}} & Monocular & Particle filter back-end\\
& \cite{Davison2007} (2007) & & &\\
& & & &\\
\textbf{MR SLAM} & \cite{Choudhary2016} (2016) & - & Multiple robots/ &\\
& \cite{Alexandre2013} (2013) & & sensors &\\
& \cite{Zhou2006} (2006) & & &\\
& \cite{Howard2006} (2006) & & &\\
& \cite{Liu2003} (2003) & & &\\
& & & &\\
\textbf{NID SLAM} & \cite{Pascoe2017} (2017) & - & Monocular & Robust to lighting and weather\\
& & & & GPU reliant\\
& & & &\\
\textbf{OKVIS} & \cite{Leutenegger2015} (2015) & {\href{https://github.com/ethz-asl/okvis_ros}{Link}} & Stereo & Focus on IMU integration\\
& \cite{Leutenegger2014} (2014) & & IMU &\\
& \cite{Leutenegger2013} (2013) & & &\\
& & & &\\
\textbf{ORB SLAM} & \cite{Mur-Artal2017} (2017) & \href{https://github.com/raulmur/ORB_SLAM2}{Link} & Monocular, & ORB descriptor\\
& \cite{Mur-Artal2016a} (2016) & & Stereo (v2), & Runs on CPU\\
& \cite{Mur-Artal2015} (2015) & & RGB-D (v2) & Extension of PTAM\\
& \cite{Mur-Artal2014} (2014) & & &\\
& & & &\\
\textbf{Pop-up SLAM} & \cite{Yang2016} (2016) & {\href{https://github.com/shichaoy/pop_up_image}{Link}} & Monocular & CNN predicts planar surfaces\\
& & & &\\
\textbf{PTAM} & \cite{Klein2007} (2007) & {\href{https://github.com/Oxford-PTAM/PTAM-GPL}{Link}} & Monocular & Parallel tracking and mapping\\
& & & &\\
\textbf{RatSLAM} & \cite{Ball2013} (2013) & {\href{https://github.com/davidmball/ratslam}{Link}} & - (back-end) & Map and pose estimation\\
& \cite{Maddern2009} (2009) & & & based on a competitive attractor\\
& \cite{Milford2008} (2008) & & & network, inspired by rat's brains\\
& \cite{Milford2006} (2006) & & &\\
& \cite{Milford2005} (2005) & & &\\
& \cite{Milford2004} (2004) & & &\\
& & & &\\
\textbf{RD SLAM} & \cite{Tan2013a} (2013) & - & Monocular & Focus on dynamic environments\\
& & & &\\
\textbf{REBVO} & \cite{Tarrio2016} (2016) & {\href{https://github.com/JuanTarrio/rebvo}{Link}} & Monocular, & Odometry on edges\\
& & & IMU &\\
& & & &\\
\textbf{REMODE} & \cite{Pizzoli2014} (2014) & {\href{https://github.com/uzh-rpg/rpg_open_remode}{Link}} & Monocular & Dense\\
& & & & GPU reliant\\
& & & &\\
\textbf{RFM SLAM} & \cite{Agarwal2016} (2016) & {\href{https://github.com/sauravag/edpl-rfmslam}{Link}} & - (back-end) & Relative feature measurements\\
& & & & Reduced complexity\\
& & & &\\
\textbf{RGB-D SLAM} & \cite{Endres2012} (2012) & {\href{https://github.com/felixendres/rgbdslam_v2}{Link}} & RGB-D &\\
& \cite{Endres2012a} (2012) & & &\\
& & & &\\
\textbf{RKSLAM} & \cite{Liu2016} (2016) & {\href{https://zjucvg.net/rkslam/rkslam.html}{Link}} & Monocular, & Robust to fast motion and rotation\\
& & & IMU &\\
& & & &\\
\textbf{ROCC} & \cite{Buczko2017} (2017) & - & Monocular, & Decouples rotation and translation\\
& \cite{Buczko2016} (2016) & & Stereo & Feature outlier removal\\
& \cite{Buczko2016a} (2016) & & & Focus on automotive\\
& & & &\\
\textbf{ROVIO} & \cite{Bloesch2015} (2014) & {\href{https://github.com/ethz-asl/rovio}{Link}} & Monocular, & Focus on IMU integration\\
& & & IMU & Relative representation\\
& & & &\\
\textbf{RSLAM} & \cite{Mei2011} (2011) & - & Stereo & Relative representation\\
& & & & No global optimization \\
& & & &\\
\textbf{ScaViSLAM} & \cite{Strasdat2011} (2011) & {\href{https://github.com/strasdat/ScaViSLAM}{Link}} & Stereo & Scale drift aware\\
& & & & through using Lie groups\\
& & & &\\
\textbf{SEIF SLAM} & \cite{Torres-Gonzalez2014} (2014) & - & - (back-end) &\\
& \cite{Walter2007} (2007) & & &\\
& & & &\\
\textbf{SeqSLAM} & \cite{bai2017} (2017) & {\href{https://github.com/subokita/OpenSeqSLAM}{Link}} & - (back-end) & Loop detection through\\
& \cite{Siam2017} (2017) & {\href{https://github.com/siam1251/Fast-SeqSLAM}{Link}} & & image sequences\\
& \cite{Sunderhauf2013} (2013) & & & Robust to extreme changes\\
& \cite{Milford2012} (2012) & & &\\
& & & RGB-D & Uses KinectFusion\\
\textbf{SLAM++} & \cite{Salas-moreno2013} (2013) & - & & Real-time object recognition\\
& & & &\\
\textbf{SlamDunk} & \cite{Fioraio2015} (2015) & {\href{https://github.com/m4nh/skimap_ros}{Link}} & RGB-D & Runs on CPU\\
& & & &\\
\textbf{SOFT} & \cite{Cvisic2015} (2015) & - & Stereo, & Odometry based on feature selection\\
& & & IMU & Separates rotation and translation\\
& & & &\\
\textbf{S-PTAM} & \cite{Pire2017} (2017) & {\href{https://github.com/lrse/sptam}{Link}} & Stereo & Robust to lighting changes\\
& \cite{Pire2015} (2015) & & & feature-based, BRISK descriptor\\
& & & &\\
\textbf{SVO} & \cite{Forster2017} (2017) & {\href{https://github.com/uzh-rpg/rpg_svo}{Link}} & Monocular & Focus on runtime (embedded devices)\\
& \cite{Forster2014a} (2014) & & & Needs a high framerate\\
& & & &\\
\textbf{UKF SLAM} & \cite{Wu2015} (2015) & - & - (back-end) &\\
& \cite{Wang2013} (2014) & & &\\
& \cite{Huang2009} (2009) & & &\\
& & & &\\
\textbf{V-LOAM} & \cite{Zhang2015} (2015) & - & Monocular, & Combination of camera and LIDAR\\
& & & LIDAR &\\
& & & &\\
\textbf{vSLAM} & \cite{Karlsson2005} (2005) & {\href{https://wiki.ros.org/vslam}{Link}} & LRF & Robustness to changes\\
& & & & Combination of particle and\\
& & & & Kalman filter in back-end\\
\end{longtable}
\newpage
%Bibiliography
\printbibliography
\end{document}