Existing　works　have　made　some　progress　in　point　cloud　registration,　but　most　of　them　measure　performance　only　on　point　cloud　pairs　with　high　overlap.　In　practical　applications,　it　is　often　difficult　to　ensure　that　the　collected　point　clouds　overlap　in　large　regions　due　to　problems　such　as　occlusion　and　noise.　Therefore,　a　good　low-overlap　point　cloud　registration　method　is　of　great　practical　significance.　However,　extracting　reliable　correspondences　from　point　clouds　has　always　been　a　challenging　task,　particularly　when　dealing　with　low-overlap　situation.　In　this　paper,　we　propose　a　novel　method　for　low-overlap　point　cloud　registration　via　efficient　correspondence　augmentation,　called　AugLPCR,　which　not　only　enhances　correspondences　with　high　confidence,　but　also　employs　confidence　weights　to　mitigate　the　impact　of　outliers.　After　the　augmentation,　the　correspondences　used　for　the　transformation　have　a　large　amount　of　inliers,　leading　to　improved　registration　performance.　Extensive　experiments　on　indoor　and　outdoor　datasets　demonstrate　that　the　proposed　AugLPCR　is　capable　of　maintaining　consistent　performance　and　achieve　results　comparable　to　or　better　than　the　state-of-the-art　methods.　©2004-2012　IEEE.