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