別紙41 (課程博士(英文))Date of submjssion (n]onlh da}',}、ear)
Jun Mlura

Depa11m引lt ofcomputel
SC】ence and lnforma110n

Sluden11D Numbel

S1ⅡgerⅡ Kunト'alnaD189302

En邑ineering
へPplicant's na1れe

Juk 71h、 2021

SupeYvisors
Yubao Liu

Abstract (Doct01')
Ti11e ofT11esis

Real‑1ime 哘Sual sinlU11aneous Localizalion and Mapping underDlnalnic EnYjroluna11S

Approx.80o words
Visual silnultaneous localizalion and n徐Pping (SLAM) is a flmdalnenlaltedm010g>' in robo{ics. augme111ed
reali1工'. computer 、'ision. and self・dhYing. visual sLAM n徐i1小' uses an onboard camera to perfom] calnera
ego‑molion eslimalion and reconslruct111e unslraclured en、'ir01Ⅱ11e11t. There are lwo basic requiremenls for＼'isual
SLAM: robuS11racking and real‑1jme perfom〕ance. Mal〕y ＼'isual sLAM algod11m〕s use 11)e rigid/slaljc l、'orldassumplion.1in〕iling a l、'ide depl0ト'n〕enlin t11e real 、、orld、 such as p01)ulaled scenes' aS 11ⅡS slrong assun〕ption
Often resU11S in poor lracking i11dYnamic enYir01Ⅱ1〕enls
ManY sludjes h小'e lhed using selnanlic i11fonl]aljon lo help l'isual sLAM red11Ce 11〕e il〕fluence of d}'nan〕icObjects. The C11aⅡe1唱e is lo lrade 0丘 Ule lracki1唱訊jⅡ〕 semantic j11fonnauon and real・time pedonna11Ce. For
exalnple、 Mask R・CNN can aC11ieYe good segmentation resU11S and help impro、'e lracking accuracy. HO、¥eYer、 it
Usualb consun]es lnuC111ime. around 20oms. The dacienC丁 of lrackil〕g is sig11jficanⅡ>,1in〕jled by 杁,aiung for lheSeelnenlalion reS1111S.＼Ne ca11 S11Ch a n]odel a hlockιd 刀10del. To aC11i飢'e robusl tracking l、、Nle keeping 111ereal‑1ilne perfomlance、、、'e proposed a l?OJ?・hlocked 刀10de/、 in 訊'1)ich 11]e lracki1唱 Process is n010nger blocked by、¥ailing for t11e semantic resU11S
TNs lhesis proposes four Yisual sLAM me1110ds、、、'11ich 訊'e exploted beaer soluuons lo {11e problems menlioned
3bcYe. TI}e fjTst いⅣo nleu)ods are developed based on lhe blocked lnodel.訊,1)ile 11)e ou〕e玲 on 11)e non・blockedn〕odel,

Firsl、 we present RTS・VSLAM、、、,1)ich ad〕jeves robust trackil〕g bN delecung and remoYi1唱 Outliers on tl)edl'na1血C objecls using pspNet and segNet、釦ld builds a slatic semanlic map by excluding dyna11〕ic objects
HO、、,eYeT,tl〕e ea'iciencx oftl)e perfom〕a11Ce is linⅡted due l0111e blocked n〕odel
Second,10 deal wilh dynalnic n01卜Pre‑defined objects,in KMOPASLAM、 we tw lo use k‑mem〕s to segment a11
Clusters or objects. we use ope11Pose lo judge W11ich clusters belong to persons. HoweYel k・means tend toOver・segmenl, a11d t11e segmentation accuracy is 、¥orse 111an selnantic segnlentation methods. Tilne consulnption
is large due to t11e blocked n〕odel,too

T11ird.、、'e present RDS・SLAM. a real・time l'isual sLAM for dYnamic enl"irolU11enls. de、'eloped based on
Semanlic segmenmtion and 11〕e non・blocIくed model. RDS・SLAM runs in real・Ⅱme b}'獣'a111aling 11〕e tracldng a11dIhe se即】〕enlalion tl)read in para11el. Accolding l0 11〕e selnantic segmenlation resU11S、 1he molion i11fom〕alion offcalures. represenled as moYi1唱 Probabili(エ. is ul)daled il〕 11)e global mal). Then 、、'e classi武' 11】e fealures in 11〕enlal) inlo d、'namjc. slalic. and U1Ⅱくnox、"n usin81hc mo゛ng plobabjlil}'.＼ve use as man、' slalic fca111res as possible
in lhe dala associalion and 11〕e bundle adi11Suncnl pl'ocess to obtain lob11S11raddng. X入le lesled 1い'o semal〕1jc

Scgmenlalion me11〕ods、 segNeland MasIく R・CNN. using 11〕e TUM 山'namjc sequcnces. RDS・SLAM can nm al
30 HZ 訊"ilh segNel.ゞmlc 0111Y a1 15 HZ 訊'i111 MaS1く R・CNN to trade offu〕e lracIくing robuslness and 11〕e leal‑1ime1〕CI'fonnance
FOU11h.訊'e plesent RDMO・SLAM、 an exlension of RDS・SLAM.訊1ⅡC11 Can rⅡn Ⅱ)e Mask R・CNN I'ersion at 30

HZ 、、'i11111〕e help of dense opljcal ao、¥. since optica1 且Ow estimaljon is fasler U捻n Mask R・CNN segmentalion.
10 gel n〕ote segn〕ented flames、、、"e predict Ⅱle selnantjc labels using dense opljcal ao、¥ and tl}e segn〕enled frames
Of Mask R・CNN. To cope witl〕 U田al0工、'n d>'nalnic objecls. X、'e also cslilnalC 11〕e 、'elodt丁 of lan山1〕arks and Ⅱ)en
ⅡSe jl as anou)er constraintl010訊'e111〕e il〕Π11Cncc of dト'namjc ob.iccls, Thcse impr0叉'enlents n〕ake RDMO・SLAM、、'ilh NlasIく R・CNN lun a1 30 HZ 、、'11ilc IくCCI)in811〕c lracking l)clf0Π〕1ancc