Semantic SLAM

Trajectory Estimation


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.

Method Setting Code APE RPE Runtime Environment
1 CT-ICP2
This method makes use of Velodyne laser scans.
 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
This method uses stereo information.
 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 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.
4 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.
Table as LaTeX | Only published Methods





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