KITTI-360

We adopt the standard Absolute Pose Error (APE) and Relative Pose Error (RPE) as metrics for evaluating pose estimation. We align the predicted trajectory to the ground truth using a rigid transformation to evaluate the APE. The RPE is evaluated between two frames with a distance of 1 meter.

• APE: Absolute Pose Error
• RPE: Relative Pose Error

	Method	Setting	Code	APE	RPE	Runtime	Environment
1	CT-ICP2		code	0.50	1.00 %	0.06 s	1 core @ 3.5 Ghz (C/C++)
P. Dellenbach, J. Deschaud, B. Jacquet and F. Goulette: CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure. 2022 International Conference on Robotics and Automation (ICRA) 2022.
2	SOFT2			0.70	0.84 %	0.1 s	4 cores @ 2.5 Ghz (C/C++)
I. Cvišić, I. Marković and I. Petrović: SOFT2: Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric. IEEE Transactions on Robotics 2022.
3	MOLA-LO + LC		code	0.72	3.97 %	0.03 s	>8 cores @ 2.5 Ghz (C/C++)
4	ORB-SLAM2			1.92	2.03 %		NVIDIA V100
R. Mur-Artal and J. Tard'{o}s: ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. TRO 2017.
5	SUMA++			3.13	2.72 %		NVIDIA V100
X. Chen, A. Milioto, E. Palazzolo, P. Gigu\`{e}re, J. Behley and C. Stachniss: SuMa++: Efficient LiDAR-based Semantic SLAM. IROS 2019.