\begin{tabular}{c | c | c | c}
{\bf Method} & {\bf Translation} & {\bf Rotation} & {\bf Runtime}\\ \hline
SOFT2 \cite{9835150} & 0.53 \% & 0.0009 [deg/m] & 0.1 s / 4 cores \\
V-LOAM \cite{Zhang2015ICRA} & 0.54 \% & 0.0013 [deg/m] & 0.1 s / 2 cores \\
LOAM \cite{Zhang2014RSS} & 0.55 \% & 0.0013 [deg/m] & 0.1 s / 2 cores \\
TVL-SLAM+ \cite{9632274} & 0.56 \% & 0.0015 [deg/m] & 0.3 s / 1 core \\
CT-ICP2 \cite{9811849} & 0.58 \% & 0.0012 [deg/m] & 0.06 s / 1 core \\
Traj-LO \cite{zheng2024traj} & 0.58 \% & 0.0014 [deg/m] & 0.1 s / 4 cores \\
GLIM \cite{koideral2021} & 0.59 \% & 0.0015 [deg/m] & 0.1 s / GPU \\
CT-ICP \cite{9811849} & 0.59 \% & 0.0014 [deg/m] & 0.06 s / 1 core \\
SDV-LOAM \cite{10086694} & 0.60 \% & 0.0015 [deg/m] & 0.06 s / 1 core \\
KISS-ICP \cite{vizzo2023ral} & 0.61 \% & 0.0017 [deg/m] & 0.05 s / 1 core \\
filter-reg \cite{zheng2023ectlo} & 0.65 \% & 0.0016 [deg/m] & 0.01 s / GPU \\
SOFT-SLAM \cite{Cvisic2017JFR} & 0.65 \% & 0.0014 [deg/m] & 0.1 s / 2 cores \\
MULLS \cite{pan2021mulls} & 0.65 \% & 0.0019 [deg/m] & 0.08 s / 4 cores \\
ELO \cite{zheng2021efficient} & 0.68 \% & 0.0021 [deg/m] & 0.005 s / GPU \\
IMLS-SLAM \cite{8460653} & 0.69 \% & 0.0018 [deg/m] & 1.25 s / 1 core \\
MC2SLAM \cite{NeuhausGCPR2018} & 0.69 \% & 0.0016 [deg/m] & 0.1 s / 4 cores \\
ISC-LOAM \cite{wang2020intensity} & 0.72 \% & 0.0022 [deg/m] & 0.1 s / 4 cores \\
FLOAM \cite{9636655} & 0.72 \% & 0.0022 [deg/m] & 0.1 s / 1 core \\
PSF-LO \cite{9561554} & 0.82 \% & 0.0032 [deg/m] & 0.2s / 4 cores \\
RADVO \cite{BenetGuinamard2020} & 0.82 \% & 0.0018 [deg/m] & 0.07 s / 1 core \\
LG-SLAM \cite{doi10.11770278364918767756} & 0.82 \% & 0.0020 [deg/m] & 0.2 s / 4 cores \\
RotRocc+ \cite{Buczko2016ITSC} & 0.83 \% & 0.0026 [deg/m] & 0.25 s / 2 cores \\
LIMO2\_GP \cite{graeter2018limo} & 0.84 \% & 0.0022 [deg/m] & 0.2 s / 2 cores \\
CAE-LO \cite{yin2020caelo} & 0.86 \% & 0.0025 [deg/m] & 2 s / 8 cores \\
GDVO \cite{ijcai17} & 0.86 \% & 0.0031 [deg/m] & 0.09 s / 1 core \\
LIMO2 \cite{graeter2018limo} & 0.86 \% & 0.0022 [deg/m] & 0.2 s / 2 cores \\
CPFG-slam \cite{ChangShu2018IV} & 0.87 \% & 0.0025 [deg/m] & 0.03 s / 4 cores \\
SOFT \cite{Cvisic2015ECMR} & 0.88 \% & 0.0022 [deg/m] & 0.1 s / 2 cores \\
RotRocc \cite{Buczko2016ITSC} & 0.88 \% & 0.0025 [deg/m] & 0.3 s / 2 cores \\
D3VO \cite{yang2020d3vo} & 0.88 \% & 0.0021 [deg/m] & 0.1 s / 1 core \\
PNDT LO \cite{Hong2017IROS} & 0.89 \% & 0.0030 [deg/m] & 0.2 s / 8 cores \\
DVSO \cite{yang2018dvso} & 0.90 \% & 0.0021 [deg/m] & 0.1 s / GPU \\
LIMO \cite{2018arXiv180707524G} & 0.93 \% & 0.0026 [deg/m] & 0.2 s / 2 cores \\
Stereo DSO \cite{wang2017stereo} & 0.93 \% & 0.0020 [deg/m] & 0.1 s / 1 core \\
IsaacElbrusGPUSLAM \cite{ELBRUS2018} & 0.94 \% & 0.0019 [deg/m] & 0.007 s / \\
OV2SLAM \cite{fer2021ov2slam} & 0.94 \% & 0.0023 [deg/m] & 0.01 s / 1 core \\
OV2SLAM \cite{fer2021ov2slam} & 0.98 \% & 0.0023 [deg/m] & 0.01 s / 8 cores \\
ROCC \cite{Buczko2016IV} & 0.98 \% & 0.0028 [deg/m] & 0.3 s / 2 cores \\
IsaacElbrusSLAM \cite{ELBRUS2018} & 0.99 \% & 0.0020 [deg/m] & 0.008 s / 3 cores \\
SuMa-MOS \cite{chen2021ral} & 0.99 \% & 0.0033 [deg/m] & 0.1s / 1 core \\
SuMa++ \cite{chen2019iros} & 1.06 \% & 0.0034 [deg/m] & 0.1 s / 1 core \\
ULF-ESGVI \cite{yoonral21} & 1.07 \% & 0.0036 [deg/m] & 0.3 s / GPU and CPU \\
cv4xv1-sc \cite{Persson2015IV} & 1.09 \% & 0.0029 [deg/m] & 0.145 s / GPU \\
VINS-Fusion \cite{qin2019a} & 1.09 \% & 0.0033 [deg/m] & 0.1s / 1 core \\
MonoROCC \cite{Buczko2017IV} & 1.11 \% & 0.0028 [deg/m] & 1 s / 2 cores \\
DEMO \cite{Zhang2014IROS} & 1.14 \% & 0.0049 [deg/m] & 0.1 s / 2 cores \\
ORB-SLAM2 \cite{murORB2} & 1.15 \% & 0.0027 [deg/m] & 0.06 s / 2 cores \\
IV-SLAM \cite{rabiee2020ivslam} & 1.17 \% & 0.0025 [deg/m] & 0.1 s / GPU \\
NOTF \cite{Deigmoeller2016GCPR} & 1.17 \% & 0.0035 [deg/m] & 0.45 s / 1 core \\
S-PTAM \cite{pire2017sptam} & 1.19 \% & 0.0025 [deg/m] & 0.03 s / 4 cores \\
S-LSD-SLAM \cite{Engel2015IROS} & 1.20 \% & 0.0033 [deg/m] & 0.07 s / 1 core \\
VoBa \cite{Tardif2010IROS} & 1.22 \% & 0.0029 [deg/m] & 0.1 s / 1 core \\
STEAM-L WNOJ \cite{tang2018white} & 1.22 \% & 0.0058 [deg/m] & 0.2 s / 1 core \\
LiViOdo \cite{2018arXiv180707524G} & 1.22 \% & 0.0042 [deg/m] & 0.5 s / 1 core \\
SLUP \cite{QU2017} & 1.25 \% & 0.0041 [deg/m] & 0.17 s / 4 cores \\
STEAM-L \cite{tang2018learning} & 1.26 \% & 0.0061 [deg/m] & 0.2 s / 1 core \\
FRVO \cite{meiqing2017vo} & 1.26 \% & 0.0038 [deg/m] & 0.03 s / 1 core \\
JFBVO-FM \cite{sardana2023improving} & 1.28 \% & 0.0010 [deg/m] & 0.1 s / 1 core \\
MFI \cite{Badino2013ICCVWORK} & 1.30 \% & 0.0030 [deg/m] & 0.1 s / 1 core \\
TLBBA \cite{Lu2013IV} & 1.36 \% & 0.0038 [deg/m] & 0.1 s / 1 Core \\
2FO-CC \cite{Kreso2015VISAPP} & 1.37 \% & 0.0035 [deg/m] & 0.1 s / 1 core \\
SALO \cite{kovalenko2019salo} & 1.37 \% & 0.0051 [deg/m] & 0.6 s / 1 core \\
SuMa \cite{behley2018rss} & 1.39 \% & 0.0034 [deg/m] & 0.1 s / 1 core \\
ProSLAM \cite{2017arXiv170904377S} & 1.39 \% & 0.0035 [deg/m] & 0.02 s / 1 core \\
ESVO \cite{9377372} & 1.42 \% & 0.0048 [deg/m] & 1 s / 1 core \\
JFBVO \cite{10.11453352593.3352651} & 1.43 \% & 0.0038 [deg/m] & 0.05 s / 1 core \\
StereoSFM \cite{Badino2011MVA} & 1.51 \% & 0.0042 [deg/m] & 0.02 s / 2 cores \\
SSLAM \cite{Bellavia2013ICIAP} & 1.57 \% & 0.0044 [deg/m] & 0.5 s / 8 cores \\
Stereo-RIVO \cite{SLAEHI} & 1.61 \% & 0.0025 [deg/m] & 0.07 s / 4 cores \\
VOLDOR \cite{Min2020CVPR} & 1.65 \% & 0.0050 [deg/m] & 0.1 s / \\
eVO \cite{Sanfourche13} & 1.76 \% & 0.0036 [deg/m] & 0.05 s / 2 cores \\
Stereo DWO \cite{Huai2015stereo} & 1.76 \% & 0.0026 [deg/m] & 0.1 s / 4 cores \\
BVO \cite{8278080} & 1.76 \% & 0.0036 [deg/m] & 0.1 s / 1 core \\
3DOF-SLAM \cite{visapp16} & 1.89 \% & 0.0083 [deg/m] & 0.02 s / 1 core \\
EfficientLO-Net \cite{wang2021efficient} & 1.92 \% & 0.0052 [deg/m] & 0.03 s / 1 core \\
D6DVO \cite{Comport07} & 2.04 \% & 0.0051 [deg/m] & 0.03 s / 1 core \\
PMO / PbT-M2 \cite{fanani2017predictive} & 2.05 \% & 0.0051 [deg/m] & 1 s / 1 core \\
GFM \cite{zhao2019good} & 2.12 \% & 0.0056 [deg/m] & 0.03 s / 2 cores \\
SSLAM-HR \cite{SSLAM0} & 2.14 \% & 0.0059 [deg/m] & 0.5 s / 8 cores \\
FTMVO \cite{Mirabdollah2015GCPR} & 2.24 \% & 0.0049 [deg/m] & 0.11 s / 1 core \\
PbT-M1 \cite{FananiBradlerOchsMester2016} & 2.38 \% & 0.0053 [deg/m] & 1 s / 1 core \\
FLVIS \cite{chen2020stereo} & 2.42 \% & 0.0057 [deg/m] & 0.05 s / 2 cores \\
VISO2-S \cite{Geiger2011IV} & 2.44 \% & 0.0114 [deg/m] & 0.05 s / 1 core \\
MLM-SFM \cite{Song2014CVPR} & 2.54 \% & 0.0057 [deg/m] & 0.03 s / 5 cores \\
GT\_VO3pt \cite{Beall10} & 2.54 \% & 0.0078 [deg/m] & 1.26 s / 1 core \\
RMCPE+GP \cite{Mirabdollah2014GCPR} & 2.55 \% & 0.0086 [deg/m] & 0.39 s / 1 core \\
KLTVO \cite{9018624} & 2.63 \% & 0.0042 [deg/m] & 0.1 s / 1 core \\
VO3pt \cite{Alcantarilla11phd} & 2.69 \% & 0.0068 [deg/m] & 0.56 s / 1 core \\
TGVO \cite{KittEtAl2010} & 2.94 \% & 0.0077 [deg/m] & 0.06 s / 1 core \\
VO3ptLBA \cite{Alcantarilla11phd} & 3.13 \% & 0.0104 [deg/m] & 0.57 s / 1 core \\
PLSVO \cite{Gomez2015} & 3.26 \% & 0.0095 [deg/m] & 0.20 s / 2 cores \\
BLF \cite{2017arXiv171206352V} & 3.49 \% & 0.0128 [deg/m] & 0.7 s / 1 core \\
CFORB \cite{Mankowitz2015cforb} & 3.73 \% & 0.0107 [deg/m] & 0.9 s / 8 cores \\
DeepCLR \cite{horn2020deepclr} & 3.83 \% & 0.0104 [deg/m] & 0.05 s / GPU \\
VOFS \cite{Kaess2009ICRA} & 3.94 \% & 0.0099 [deg/m] & 0.51 s / 1 core \\
VOFSLBA \cite{Kaess09icra} & 4.17 \% & 0.0112 [deg/m] & 0.52 s / 1 core \\
CUDA-EgoMotion \cite{aguilar2019fastest} & 4.36 \% & 0.0052 [deg/m] & .001 s / GPU \\
BCC \cite{2017arXiv171206352V} & 4.59 \% & 0.0175 [deg/m] & 1 s / 1 core \\
D3DLO \cite{adis2021d3dlo} & 5.40 \% & 0.0154 [deg/m] & 0.1 s / GPU \\
EB3DTE+RJMCM \cite{Boukhers20171} & 5.45 \% & 0.0274 [deg/m] & 1 s / 1 core \\
LTMVO \cite{zou2020learning} & 7.40 \% & 0.0142 [deg/m] & 0.1 s / 1 core \\
VISO2-M + GP \cite{Geiger11} & 7.46 \% & 0.0245 [deg/m] & 0.15 s / 1 core \\
BLO \cite{2017arXiv171206352V} & 9.21 \% & 0.0163 [deg/m] & 0.1 s / 1 core \\
VISO2-M \cite{Geiger2011IV} & 11.94 \% & 0.0234 [deg/m] & 0.1 s / 1 core \\
MonoDepth2 \cite{godard2019digging} & 12.59 \% & 0.0312 [deg/m] & 1 s / 1 core \\
SMD-LVO \cite{slinko2019scene} & 13.25 \% & 0.0097 [deg/m] & 0.03 s / GPU \\
SC-SfMLearner (cs+k) \cite{bian2019unsupervised} & 13.69 \% & 0.0355 [deg/m] & 0.01 s / 1 core \\
CC \cite{ranjan2019competitive} & 16.06 \% & 0.0320 [deg/m] & 0.1 s / 1 core \\
OABA \cite{Frost2016ICRA} & 20.95 \% & 0.0135 [deg/m] & 0.5 s / 1 core \\
SC-SfMLearner (k) \cite{bian2019unsupervised} & 21.47 \% & 0.0425 [deg/m] & 0.01 s / 1 core \\
SLL \cite{DBLPjournalscorrabs210808516} & 90.05 \% & 0.2645 [deg/m] & 0.1 s / 1 core \\
stereo-Indirect \cite{ERROR: Wrong syntax in BIBTEX file.} & 97.22 \% & 0.2685 [deg/m] & 0.7 s / 2 cores
\end{tabular}