{"id":"https://openalex.org/W7125177235","doi":"https://doi.org/10.48550/arxiv.2601.12122","title":"Active Semantic Mapping of Horticultural Environments Using Gaussian Splatting","display_name":"Active Semantic Mapping of Horticultural Environments Using Gaussian Splatting","publication_year":2026,"publication_date":"2026-01-17","ids":{"openalex":"https://openalex.org/W7125177235","doi":"https://doi.org/10.48550/arxiv.2601.12122"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.12122","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.12122","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.12122","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042487921","display_name":"Jose Cuaran","orcid":"https://orcid.org/0000-0002-7281-9109"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cuaran, Jose","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123521354","display_name":"Naveen K. Upalapati","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Upalapati, Naveen K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123464000","display_name":"Girish Chowdhary","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chowdhary, Girish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.6365000009536743,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.6365000009536743,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.16660000383853912,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.05460000038146973,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/robustness","display_name":"Robustness (evolution)","score":0.5813999772071838},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5734000205993652},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5580999851226807},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.49480000138282776},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.384799987077713},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.37209999561309814},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.36579999327659607},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.36480000615119934},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.361299991607666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8723000288009644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6446999907493591},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5813999772071838},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5655999779701233},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.384799987077713},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.36579999327659607},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2540999948978424},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.12122","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.12122","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.12122","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.12122","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7758392691612244,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"reconstruction":[1,44,139,189],"of":[2,100,158],"agricultural":[3,191],"scenes":[4],"plays":[5],"a":[6,30,50,73,98,150,177],"vital":[7],"role":[8],"in":[9,33,134,160,180,190],"tasks":[10],"such":[11],"as":[12],"phenotyping":[13],"and":[14,68,84,93,120,138,145,167],"yield":[15],"estimation.":[16,147],"However,":[17],"traditional":[18],"approaches":[19,133],"that":[20,127],"rely":[21],"on":[22],"manual":[23],"scanning":[24],"or":[25],"fixed":[26],"camera":[27],"setups":[28],"remain":[29],"major":[31],"bottleneck":[32],"this":[34,37],"process.":[35],"In":[36],"work,":[38],"we":[39],"propose":[40],"an":[41,156],"active":[42],"3D":[43,62,87,101],"framework":[45],"for":[46,80,103,185],"horticultural":[47],"environments":[48],"using":[49],"mobile":[51],"manipulator.":[52],"The":[53],"proposed":[54],"system":[55],"integrates":[56],"the":[57],"classical":[58],"Octomap":[59,75],"representation":[60],"with":[61],"Gaussian":[63,88],"Splatting":[64,89],"to":[65,96,114,149,169],"enable":[66],"accurate":[67],"efficient":[69],"target-aware":[70],"mapping.":[71],"While":[72],"low-resolution":[74],"provides":[76],"probabilistic":[77],"occupancy":[78],"information":[79,95],"informative":[81],"viewpoint":[82],"selection":[83],"collision-free":[85],"planning,":[86],"leverages":[90],"geometric,":[91],"photometric,":[92],"semantic":[94,188],"optimize":[97],"set":[99],"Gaussians":[102],"high-fidelity":[104],"scene":[105],"reconstruction.":[106],"We":[107],"further":[108],"introduce":[109],"simple":[110],"yet":[111],"effective":[112],"strategies":[113],"enhance":[115],"robustness":[116],"against":[117],"segmentation":[118,172],"noise":[119],"reduce":[121],"memory":[122],"consumption.":[123],"Simulation":[124],"experiments":[125],"demonstrate":[126],"our":[128,153],"method":[129],"outperforms":[130],"purely":[131],"occupancy-based":[132],"both":[135],"runtime":[136],"efficiency":[137],"accuracy,":[140],"enabling":[141],"precise":[142],"fruit":[143],"counting":[144],"volume":[146],"Compared":[148],"0.01m-resolution":[151],"Octomap,":[152],"approach":[154],"achieves":[155,176],"improvement":[157],"6.6%":[159],"fruit-level":[161],"F1":[162],"score":[163],"under":[164,171],"noise-free":[165],"conditions,":[166],"up":[168],"28.6%":[170],"noise.":[173],"Additionally,":[174],"it":[175],"50%":[178],"reduction":[179],"runtime,":[181],"highlighting":[182],"its":[183],"potential":[184],"scalable,":[186],"real-time":[187],"robotics.":[192]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-22T00:00:00"}
