It　remains　challenging　to　assist　BVI　individuals　in　outdoor　travel　nowadays.In　this　paper,　We　propose　a　set　of　low-cost　wearable　obstacle　avoidance　devices　and　introduce　an　obstacle　detection　algorithm　called　L-PointPillars,　which　is　based　on　point　cloud　data　and　is　suitable　for　edge　devices.　We　first　model　the　obstacles　faced　by　BVI　individuals　during　outdoor　travel　and　then　establish　a　mapping　between　the　information　space　and　physical　space　based　on　point　clouds.　We　then　introduce　depthwise　separable　convolution　and　attention　mechanisms　to　develop　L-PointPillars,　a　fast　neural　network　for　obstacle　detection.　This　network　is　specifically　designed　for　creating　wearable　obstacle　detection　devices.　Finally,　we　implemented　a　wearable　electronic　travel　aid　device　(WETAD)　based　on　L-PointPillars　on　the　Jetson　Xavier　NX.　Experiments　show　that　while　L-PointPillars　reduces　the　number　of　parameters　in　the　original　PointPillars　by　75%,　WETAD　achieves　an　average　obstacle　detection　accuracy　of　95.3%.　It　takes　an　average　of　144　milliseconds　to　process　each　frame　during　outdoor　travel　for　BVI　individuals,　which　is　more　than　twice　as　fast　as　the　Second　network　and　31%　improvement　compared　to　PointPillars.　©　2023,　China　Computer　Federation　(CCF).