Magnetic　tracking　approach　(MTA)　is　mainly　based　on　the　observation　of　the　magnetic　field　produced　by　a　magnetic　tracking　target.　A　permanent　magnet　is　usually　employed　as　the　tracking　target,　and　a　magnetic　dipole　model　is　used　to　estimate　the　magnetic　field.　In　this　article,　we　introduce　an　intuitive　method　to　solve　the　magnetic　tracking　problem　based　on　graph　optimization.　A　graph　is　constructed　to　formulate　the　tracking　problem,　whose　nodes　correspond　to　the　poses　of　the　permanent　magnet　at　different　times　and　whose　edges　represent　constraints　derived　by　sensor　measurements.　The　accuracy　and　robustness　of　MTA　play　a　vital　role　in　biomedical　and　industrial　applications.　However,　a　potential　abnormal　edge　will　appear　in　the　graph　when　a　magnetic　sensor　is　disturbed　by　an　interference　source　like　a　small　magnet,　which　will　deteriorate　the　localization　accuracy　of　MTA.　To　improve　its　robustness,　Huber　cost　function　is　used　to　reduce　the　weight　of　the　abnormal　edge.　Finally,　experiments　were　carried　out　to　verify　the　performance　of　the　proposed　approach　with　and　without　magnetic　field　interference.　Comparing　the　localization　errors　with　the　MTA　based　on　the　standard　Levenberg-Marquardt　algorithm,　the　results　illustrate　that　the　proposed　approach　could　achieve　superior　localization　accuracy　and　anti-interference　ability.