The KITTI Vision Benchmark Suite

Method

Two-view Only Pose Graph Optimization [lc] [2GO]
[Anonymous Submission]

Submitted on 21 Jan. 2025 06:11 by
[Anonymous Submission]

Running time:		0.15 s
Environment:		1 core @ >3.5 Ghz (Python)

Method Description:

We investigate a scalable VSLAM approach without
mapping/reconstruction that refines an existing
trajectory, based solely two-view loop closures. The
"map" is simply a sparse keyframed pose graph
without dense geometry representations (e.g point
clouds).

In particular, we leverage recent advancements in
image matching and monocular metric depth estimation
for accurate trajectory optimization from two-view
edges. In this submission, we use 2GO to refine a
VINS-Fusion odometry.

Parameters:

kf_config:
trans_th_m: 5.0
rot_th_rad: 0.5
image retrieval:
top_k_similarity: 10
top_k_proximity: 10
min_baseline_m: 1.0
max_angle_between_cams_deg: 90.0
time_between_lcs_s: 1.0
proximity_threshold_m: 25.0
proximity_threshold_deg: 90.0
triangulate_cfg:
min_match_score: 0.8
min_inlier_matches: 100
max_valid_metric_depth: 30.0
min_valid_metric_depth: 0.1
min_valid_metric_conf: 0.9
min_valid_points_for_absolute_pose: 20
absolute_pose_estimation_method: 3d_2d
pose_graph_cfg:
prior_xyz_sigma: 0.05
prior_rpy_sigma: 0.5
odometry_xyz_sigma: 0.1
odometry_rpy_sigma: 1.0
rel_loop_close_xyz_sigma: 0.2
abs_loop_close_xyz_sigma: 2.0
loop_close_rpy_sigma: 2.5
abs_trans_err_m: 12.5
trans_vect_err_deg: 10.0
rot_err_deg: 10.0
use_robust_noise: true
lc_type: both

Latex Bibtex:

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.