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Efficient Graduated Non-Convexity for Pose Graph Optimization (EGNC-PGO)

arXiv

EGNC-PGO showed better result in challenging case than the former approach

Structure

  • experiments: Contains the implementations for experiments.
  • risam: Contains the implementation of the algorithm.

Building Instructions (Validated as of Oct 2023)

  • Version Summary (tested and confirmed with the following dependency versions)

    • GTSAM: Tag=4.2a8, exact hash=9902ccc0a4f62123e91f057babe3612a95c15c20
    • KimeraRPGO: exact hash=8c5e163ba38345ff583d87403ad53bf966c0221b
    • dcsam: exact hash=b7f62295eec201fb00ee6d1d828fa551ac1f4bd7
    • GCC: 11.4.0

  • These should be checked out when the git submodules are initialized, but are included here for completeness

  • GTSAM

    • Download GTSAM version 4.2a8 ! 4.2a9 not working!
    • Setup compile time options required by KimeraRPGO
    • Build and optionally install GTSAM (Scripts assume GTSAM python is installed in the active python environment)

  • Clone EGNC-PGO and Submodules

    • git clone --recursive https://github.com/SNU-DLLAB/EGNC-PGO.git

  • Build GTSAM

    • Configure cmake with following options: cmake .. -DGTSAM_POSE3_EXPMAP=ON -DGTSAM_ROT3_EXPMAP=ON -DGTSAM_USE_SYSTEM_EIGEN=ON

  • Link GTSAM

    • If you install GTSAM this should be automatic
    • If you are working with a local build of GTSAM set GTSAM_DIR and GTSAM_INCLUDE_DIR to the appropriate directories.

  • Build EGNC-PGO with riSAM

    • cd EGNC-PGO
    • mkdir build
    • cd build
    • cmake ..
    • make

Acknowlodgement

The original code is from "Robot Perception Lab - Carnegie Mellon University". link: https://github.com/rpl-cmu/risam