The　processing　of　3D　point　clouds　from　laser　scanning　is　still　challenging　in　self-driving　perception　and　positioning　community.　Ground　segmentation　is　the　key　topic,　splitting　out　the　ground　point　cloud　effectively　reduces　the　amount　of　data　and　increases　the　speed　of　subsequent　point　cloud　clustering　and　feature　point　extraction.　The　ground　points　segmented　in　SLAM　can　be　used　as　constraints　for　back-end　optimisation,　improving　the　accuracy　of　map　building　and　localisation.　After　the　ground　segmentation　is　completed,　it　can　be　used　as　a　passable　area　for　vehicle　path　planning.　Existing　approaches　are　based　on　the　assumption　that　the　ground　is　plane,　but　unfortunately　the　ground　is　not　a　plane,　with　a　large　number　of　slopes,　roadsides,　and　parts　of　the　ground　even　rugged　and　full　of　obstacles.　In　order　to　solve　the　ground　segmentation　problem,　this　paper　proposes　a　ground　segmentation　method　based　on　polar　grid.　The　main　contributions　of　this　paper　include:　(1)　We　divide　a　frame　of　point　cloud　space　into　several　regions,　each　of　which　is　divided　into　several　grids,　based　on　the　scanning　characteristics　of　the　LIDAR.　(2)　The　plane　was　fitted　based　on　the　improved　RANSAC　algorithm　for　each　of　the　previously　divided　grids.　Experiments　with　KITTI　and　campus　real　environment　datasets　show　that　the　sensitivity　of　our　proposed　method　can　reach　more　than　98%　and　the　specificity　is　below　10%.　The　proposed　algorithm　can　effectively　and　correctly　separate　the　ground　from　the　point　cloud,　even　on　slopes　and　the　ground　with　many　obstacles.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.