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+using+only+an+instantaneous+measurement+from+a+miniature+optical+time-of-flight+sensor.+The+key+to+our+method+is+to+utilize+the+entirety+of+information+encoded+in+raw+time-of-flight+data+captured+by+off-the-shelf+distance+sensors.+We+provide+an+analysis+of+the+problem+in+which+we+identify+the+key+ambiguity+between+geometry+and+surface+photometrics.+To+overcome+this+challenging+ambiguity%252C+we+fit+a+Gaussian+mixture+model+to+a+small+dataset+of+planar+surface+measurements.+This+model+implicitly+captures+the+expected+geometry+and+distribution+of+photometrics+of+the+planar+surface+and+is+used+to+identify+measurements+that+are+likely+to+contain+deviations.+We+characterize+our+method+on+a+variety+of+surfaces+and+planar+deviations+across+a+range+of+scenarios.+We+find+that+our+method+utilizing+raw+time-of-flight+data+outperforms+baselines+which+use+only+derived+distance+estimates.+We+build+an+example+application+in+which+our+method+enables+mobile+robot+obstacle+and+cliff+avoidance+over+a+wide+field-of-view.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7f4fcbb748cc99e574dce97f4a86512f%3frik%3dFtKxOhjiMD%252bFuw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Surface+Deviation+Sensor&FORM=IRPRST&ck=670CA79651173875510777611D1CE1FE&selectedIndex=25&itb=0)
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