Skip to content

baidu31/DCReg

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

63 Commits
 
 
 
 
 
 

Repository files navigation

DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration

Xiangcheng Hu1 · Xieyuanli Chen2 · Mingkai Jia1 · Jin Wu 3*
Ping Tan1· Steven L. Waslander4†

1HKUST   2NUDT   3USTB    4U of T
†Project lead *Corresponding author

arXivvideoGitHub Stars GitHub Issues

image-20250908194440555

DCReg (Decoupled Characterization for ill-conditioned Registration) is a principled framework that addresses ill-conditioned point cloud registration problems, achieving 20% - 50% accuracy improvement and 5-100 times speedup over state-of-the-art methods.

  • Reliable ill-conditioning detection: Decouples rotation and translation via Schur complement decomposition for ill-conditioning detection,eliminating coupling effects that mask degeneracy patterns.
  • Quantitative characterization: Maps mathematical eigenspace to physical motion space, revealing which and to what extent specific motions lack constraints
  • Targeted mitigation: Employs targeted preconditioning that stabilizes only degenerate directions while preserving observable information.

DCReg seamlessly integrates with existing registration pipelines through an efficient PCG solver with a single interpretable parameter.

Timeline

2025/09/09: the preprint paper is online, baseline codes will be published first!

Methods

image-20250913000818366

image-20250913001002278 image-20250913000856227

Baseline and dataset

image-20250909214128111
image-20250908194514540 image-20250908194526477

Video demo

image-20250910212340395

Scenarios Characterization Example Features
pk01_dcreg_seg image-20250910213549613 Planar degeneracy,
t0-t1-r2 degenerate,
the main
components
of motion
sources are
X-Y-Yaw. e.g.
t0 = 90.0% X
+ xx %Y + xx% Z.
the related
angles of
X with t0
is 4.5 deg, that
means X
should be the
main reason.
see figure 16.
image-20250910213208822 narrow stairs, spares
features cause this
degeneracy. sometimes
t2, sometimes r0-r1.
see
figure 17.
corridor_dcreg_x5 image-20250910213259165 narrow passage,
r0-t0 or r0, depends
on your
measurements.
dcreg_x50 image-20250910213415142 rich features but
within narrow
environments.
r0-t0 or r0.

Controlled Simulation Analysis

image-20250908194819193
image-20250908194834002
image-20250908194848247 image-20250908194901218

Real-world Performance Evaluation

localization and mapping

image-20250908195036175

image-20250908195103021 image-20250908195117064

Degeneracy Characterization

image-20250908195356150
image-20250908195410597

Degeneracy Detection

image-20250908195304202

image-20250908195247186

image-20250908195226346 image-20250908195236593

Ablation and Hybrid Analysis

image-20250908195458538 image-20250908195511133

Run-time analysis

image-20250908195549384 image-20250908195600116

Parameter

image-20250913000546827

Citations

For referencing our work, please use:

@misc{hu2025dcreg,
      title={DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration}, 
      author={Xiangcheng Hu and Xieyuanli Chen and Mingkai Jia and Jin Wu and Ping Tan and Steven L. Waslander},
      year={2025},
      eprint={2509.06285},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2509.06285}, 
}

Acknowledgment

The authors gratefully acknowledge the valuable contributions that made this work possible.

  • We extend special thanks to Dr. Binqian Jiang and Dr. Jianhao Jiao for their insightful discussions that significantly contributed to refining the theoretical framework presented in this paper.
  • We also appreciate Mr. Turcan Tuna for his technical assistance with the baseline algorithm implementation.

Contributors

About

DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%