Adaptive Graduated Non-Convexity for Pose Graph Optimization (AGNC-PGO)

Presented in IROS 2023 Workshop "ROBOTIC PERCEPTION AND MAPPING: FRONTIER VISION & LEARNING TECHNIQUES".

Structure

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

Building Instructions (Validated as of Aug 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 riSAM and Submodules

  • git clone --recursive https://github.com/SNU-DLLAB/AGNC-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 AGNC-PGO with riSAM

  • cd AGNC-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