{"id":"https://openalex.org/W2890608517","doi":"https://doi.org/10.1109/icra.2018.8460488","title":"Pilot Surveys for Adaptive Informative Sampling","display_name":"Pilot Surveys for Adaptive Informative Sampling","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2890608517","doi":"https://doi.org/10.1109/icra.2018.8460488","mag":"2890608517"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8460488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008550555","display_name":"Stephanie Kemna","orcid":"https://orcid.org/0000-0001-8679-542X"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephanie Kemna","raw_affiliation_strings":["Computer Science department, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science department, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005091065","display_name":"Oliver Kroemer","orcid":"https://orcid.org/0000-0003-2007-3867"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oliver Kroemer","raw_affiliation_strings":["Computer Science department, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science department, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077367921","display_name":"Gaurav S. Sukhatme","orcid":"https://orcid.org/0000-0003-2408-474X"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gaurav S. Sukhatme","raw_affiliation_strings":["Computer Science department, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science department, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.5352,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.97724183,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"6417","last_page":"6424"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9724000096321106,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7198469638824463},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7184283137321472},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6118860244750977},{"id":"https://openalex.org/keywords/waypoint","display_name":"Waypoint","score":0.5523932576179504},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.5386564135551453},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5375046730041504},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5034453272819519},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44266757369041443},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42926162481307983},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42903101444244385},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4283643960952759},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4067630171775818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36046046018600464},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28039294481277466},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.2647700905799866},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12267875671386719},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.08883470296859741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7198469638824463},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7184283137321472},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6118860244750977},{"id":"https://openalex.org/C2781271823","wikidata":"https://www.wikidata.org/wiki/Q138081","display_name":"Waypoint","level":2,"score":0.5523932576179504},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.5386564135551453},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5375046730041504},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5034453272819519},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44266757369041443},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42926162481307983},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42903101444244385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4283643960952759},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4067630171775818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36046046018600464},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28039294481277466},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2647700905799866},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12267875671386719},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.08883470296859741},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2018.8460488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.8600000143051147,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W301805053","https://openalex.org/W2018182198","https://openalex.org/W2021774297","https://openalex.org/W2032239956","https://openalex.org/W2130902106","https://openalex.org/W2143088799","https://openalex.org/W2166295994","https://openalex.org/W2169645448","https://openalex.org/W2187219784","https://openalex.org/W2543131193","https://openalex.org/W2606385533","https://openalex.org/W2801768210","https://openalex.org/W2962752450","https://openalex.org/W3003506411","https://openalex.org/W4211049957","https://openalex.org/W4214717370","https://openalex.org/W4236236480","https://openalex.org/W6610558371","https://openalex.org/W6631084031","https://openalex.org/W6679608865","https://openalex.org/W6681399333","https://openalex.org/W6684953212","https://openalex.org/W6686802380","https://openalex.org/W6696984291","https://openalex.org/W6728949768","https://openalex.org/W6736602418","https://openalex.org/W7042407543"],"related_works":["https://openalex.org/W2141609920","https://openalex.org/W4220873401","https://openalex.org/W2912851808","https://openalex.org/W4294619368","https://openalex.org/W4380558509","https://openalex.org/W4286748465","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W3118984993","https://openalex.org/W2144336328"],"abstract_inverted_index":{"Adaptive":[0],"sampling":[1,28,104],"has":[2],"been":[3],"shown":[4],"to":[5,59,87,114,126,134,147,151,191],"be":[6,60,88],"an":[7],"effective":[8],"method":[9,121],"for":[10,55,138,175],"modeling":[11],"environmental":[12],"fields,":[13],"such":[14,46],"as":[15,47],"algae":[16],"concentrations":[17],"in":[18],"the":[19,81,140,167],"ocean.":[20],"In":[21,153],"adaptive":[22,103],"sampling,":[23],"a":[24,128,145,163],"robot":[25],"adapts":[26],"its":[27],"trajectory":[29],"based":[30],"on":[31,166],"data":[32,38,93,98,124,174],"that":[33,181,184],"it":[34],"is":[35,39,94,110,125],"collecting.":[36],"This":[37,78,131],"often":[40],"aggregated":[41],"into":[42],"models,":[43],"using":[44,75],"techniques":[45,105],"Gaussian":[48],"Process":[49],"(G":[50],"P)":[51],"regression.":[52],"The":[53],"(hyper-)parameters":[54],"these":[56],"models":[57,201],"need":[58,86],"manually":[61],"set":[62],"or,":[63],"ideally,":[64],"estimated":[65,74],"from":[66],"data.":[67,77],"For":[68],"GP":[69,196],"regression,":[70],"hyperparameters":[71,85],"are":[72],"typically":[73],"prior":[76,92,97],"paper":[79],"addresses":[80],"case":[82],"where":[83,149],"initial":[84],"estimated,":[89],"but":[90,142],"no":[91,111],"available.":[95],"Without":[96],"or":[99],"accurately":[100],"pre-defined":[101],"hyperparameters,":[102,197],"may":[106],"fail,":[107],"because":[108],"there":[109],"good":[112],"model":[113,146],"base":[115],"path":[116],"planning":[117],"decisions":[118],"on.":[119],"One":[120],"of":[122,195],"gathering":[123],"perform":[127],"pilot":[129,159,182],"survey.":[130],"survey":[132],"needs":[133],"select":[135],"informative":[136],"samples":[137],"initiating":[139],"model,":[141],"without":[143],"having":[144],"determine":[148],"best":[150],"sample.":[152],"this":[154],"work,":[155],"we":[156],"evaluate":[157],"four":[158],"surveys,":[160],"which":[161],"use":[162],"softmax":[164],"function":[165],"distance":[168],"between":[169],"waypoints":[170],"and":[171,198],"previously":[172],"sampled":[173],"waypoint":[176,186],"selection.":[177],"Simulation":[178],"results":[179],"show":[180],"surveys":[183],"maximize":[185],"spread":[187],"over":[188],"randomization":[189],"lead":[190],"more":[192,202],"stable":[193],"estimation":[194],"create":[199],"accurate":[200],"quickly.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
