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+robotic+dataset%252C+for+training+and+evaluating+their+dynamic+scene+understanding+algorithms.+Specifically%252C+the+THUD+dataset+construction+is+first+detailed%252C+including+organization%252C+acquisition%252C+and+annotation+methods.+It+comprises+both+real-world+and+synthetic+data%252C+collected+with+a+real+robot+platform+and+a+physical+simulation+platform%252C+respectively.+Our+current+dataset+includes+13+larges-scale+dynamic+scenarios%252C+90K+image+frames%252C+20M+2D%252F3D+bounding+boxes+of+static+and+dynamic+objects%252C+camera+poses%252C+and+IMU.+The+dataset+is+still+continuously+expanding.+Then%252C+the+performance+of+mainstream+indoor+scene+understanding+tasks%252C+e.g.+3D+object+detection%252C+semantic+segmentation%252C+and+robot+relocalization%252C+is+evaluated+on+our+THUD+dataset.+These+experiments+reveal+serious+challenges+for+some+robot+scene+understanding+tasks+in+dynamic+scenes.+By+sharing+this+dataset%252C+we+aim+to+foster+and+iterate+new+mobile+robot+algorithms+quickly+for+robot+actual+working+dynamic+environment%252C+i.e.+complex+crowded+dynamic+scenes.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.eebaa3e02255202c2617138e219803e7%3frik%3d96DaRSOCZ%252fDkVg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Robot+Indoor+Dataset&FORM=IRPRST&ck=E4DCC275CBF23453DE516F5CA9EDC9E6&selectedIndex=0&itb=0)
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