15.3.1.9 Loop Closure, Simultaneous Localization and Mapping

Chapter Contents (Back)
Loop Closure.
See also Visual SLAM: Simultaneous Location and Mapping or Matching.

Kimera,
2019
WWW Link. Code, SLAM. 1910
Kimera is implemented in C++, is ROS-compatible, and runs on CPU. It only uses a camera and an IMU. The code performs real-time visual-inertial SLAM (including robust loop closure detection and outlier removal) and builds a semantically labeled 3D mesh. Kimera is a hybrid creature and includes state-of-the-art algorithms for visual-inertial odometry, robust pose graph optimization, real-time mesh reconstruction, and 3D semantic segmentation.

Ho, K.L.[Kin Leong], Newman, P.[Paul],
Detecting Loop Closure with Scene Sequences,
IJCV(74), No. 3, September 2007, pp. 261-286.
Springer DOI 0707
Assert that the robot has returned to the original location. BibRef

Eade, E.D.[Ethan D.], Drummond, T.W.[Tom W.],
Edge landmarks in monocular SLAM,
IVC(27), No. 5, 2 April 2009, pp. 588-596.
Elsevier DOI 0904
BibRef
Earlier: BMVC06(I:7).
PDF File. 0609
BibRef
And:
Unified Loop Closing and Recovery for Real Time Monocular SLAM,
BMVC08(xx-yy).
PDF File. 0809
BibRef
Earlier:
Monocular SLAM as a Graph of Coalesced Observations,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
Scalable Monocular SLAM,
CVPR06(I: 469-476).
IEEE DOI 0606
SLAM; Monocular SLAM; Structure and motion; Edges; Landmarks; Particle filter; Edgelet; Partial initialization; Inverse depth; Data association; Simultaneous localization and mapping; Edge detection; Monocular vision Simultaneous Location and Map BibRef

Avraham, G., Zuo, Y., Dharmasiri, T., Drummond, T.W.[Tom W.],
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points,
ICCV19(8119-8128)
IEEE DOI 2004
convolutional neural nets, image recognition, learning (artificial intelligence), active vision dataset, Navigation BibRef

Williams, B.[Brian], Klein, G.[Georg], Reid, I.D.[Ian D.],
Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM,
PAMI(33), No. 9, September 2011, pp. 1699-1712.
IEEE DOI 1109
BibRef
Earlier:
Real-Time SLAM Relocalisation,
ICCV07(1-8).
IEEE DOI 0710
Turn cheap cameras into pose sensors for robotics and augmented reality. BibRef

Weise, T.[Thibaut], Wismer, T.[Thomas], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Online loop closure for real-time interactive 3D scanning,
CVIU(115), No. 5, May 2011, pp. 635-648.
Elsevier DOI 1103
BibRef
Earlier:
In-hand scanning with online loop closure,
3DIM09(1630-1637).
IEEE DOI 0910
3D modeling; 3D scanning; Registration; Integration; Loop closure Freely turn object in front of 3D scanner. BibRef

Meidow, J.[Jochen],
Effcient Multiple Loop Adjustment for Computer Vision Tasks,
PFG(2012), No. 5, 2012, pp. 501-510.
WWW Link. 1211
BibRef
And:
Efficient Video Mosaicking by Multiple Loop Closing,
PIA11(1-12).
Springer DOI 1110
BibRef

Ravari, A.N., Taghirad, H.D.,
Loop Closure Detection by Algorithmic Information Theory: Implemented on Range and Camera Image Data,
Cyber(44), No. 10, October 2014, pp. 1938-1949.
IEEE DOI 1410
cameras BibRef

Erhan, C.[Can], Sariyanidi, E.[Evangelos], Sencan, O.[Onur], Temeltas, H.[Hakan],
Patterns of approximated localised moments for visual loop closure detection,
IET-CV(11), No. 3, April 2017, pp. 237-245.
DOI Link 1704
Back to the same location. BibRef

Esfahani, M.A.[Mahdi Abolfazli], Wu, K.Y.[Ke-Yu], Yuan, S.H.[Sheng-Hai], Wang, H.[Han],
DeepDSAIR: Deep 6-DOF camera relocalization using deblurred semantic-aware image representation for large-scale outdoor environments,
IVC(89), 2019, pp. 120-130.
Elsevier DOI 1909
Deep Neural Network (DNN), Convolutional Neural Network (CNN), Camera relocalization, Pose regression, Loop closure, Visual odometry BibRef

Guclu, O.[Oguzhan], Can, A.B.[Ahmet Burak],
k-SLAM: A fast RGB-D SLAM approach for large indoor environments,
CVIU(184), 2019, pp. 31-44.
Elsevier DOI 1906
BibRef
Earlier:
A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods,
ICIAR15(297-305).
Springer DOI 1507
SLAM, RGB-D, Autocorrelogram, Hierarchical indexing, Loop closure, Real-time BibRef

Guclu, O.[Oguzhan], Can, A.B.[Ahmet Burak],
Integrating global and local image features for enhanced loop closure detection in RGB-D SLAM systems,
VC(36), No. 6, June 2020, pp. 1271-1290.
WWW Link. 2005
BibRef

Wang, Y.W.[Yu-Wei], Qiu, Y.Y.[Yuan-Ying], Cheng, P.T.[Pei-Tao], Duan, X.C.[Xue-Chao],
Robust Loop Closure Detection Integrating Visual-Spatial-Semantic Information via Topological Graphs and CNN Features,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Yang, Z.[Zhe], Pan, Y.[Yun], Deng, L.[Lei], Xie, Y.[Yuan], Huan, R.H.[Ruo-Hong],
PLSAV: Parallel loop searching and verifying for loop closure detection,
IET-ITS(15), No. 5, 2021, pp. 683-698.
DOI Link 2106
BibRef

Chen, S.B.[Shou-Bin], Zhou, B.[Baoding], Jiang, C.H.[Chang-Hui], Xue, W.X.[Wei-Xing], Li, Q.Q.[Qing-Quan],
A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Yuan, Z.[Zhian], Xu, K.[Ke], Zhou, X.Y.[Xiao-Yu], Deng, B.[Bin], Ma, Y.X.[Yan-Xin],
SVG-Loop: Semantic-Visual-Geometric Information-Based Loop Closure Detection,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
mportant component of visual simultaneous localization and mapping BibRef

Yu, M.F.[Ming-Fei], Zhang, L.[Lei], Wang, W.F.[Wu-Fan], Huang, H.[Hua],
Loop Closure Detection by Using Global and Local Features With Photometric and Viewpoint Invariance,
IP(30), 2021, pp. 8873-8885.
IEEE DOI 2111
In SLAM. Feature extraction, Visualization, Task analysis, Lighting, Shape, Image color analysis, local feature BibRef

Karam, S., Lehtola, V., Vosselman, G.,
Simple loop closing for continuous 6DOF LIDAR&IMU graph SLAM with planar features for indoor environments,
PandRS(181), 2021, pp. 413-426.
Elsevier DOI 2110
SLAM, Loop closure, Planar features, Indoor, Point clouds, Mobile mapping, Laser scanner, LIDAR, 6DOF BibRef

Qin, C.[Cao], Zhang, Y.Z.[Yun-Zhou], Liu, Y.D.[Ying-Da], Lv, G.H.[Guang-Hao],
Semantic loop closure detection based on graph matching in multi-objects scenes,
JVCIR(76), 2021, pp. 103072.
Elsevier DOI 2104
Loop closure detection, Object detection, Semantic, Simultaneous localization and mapping (SLAM), Graph matching BibRef

Zhang, K.N.[Kai-Ning], Jiang, X.Y.[Xing-Yu], Ma, J.Y.[Jia-Yi],
Appearance-Based Loop Closure Detection via Locality-Driven Accurate Motion Field Learning,
ITS(23), No. 3, March 2022, pp. 2350-2365.
IEEE DOI 2203
Feature extraction, Liquid crystal displays, Visualization, Task analysis, Robots, Simultaneous localization and mapping, autonomous vehicle BibRef

Ma, J.Y.[Jia-Yi], Ye, X.Y.[Xin-Yu], Zhou, H.[Huabing], Mei, X.G.[Xiao-Guang], Fan, F.[Fan],
Loop-Closure Detection Using Local Relative Orientation Matching,
ITS(23), No. 7, July 2022, pp. 7896-7909.
IEEE DOI 2207
Liquid crystal displays, Feature extraction, Visualization, Task analysis, Neural networks, ASMK BibRef


Peltomäki, J.[Jukka], Ni, X.Y.[Xing-Yang], Puura, J.[Jussi], Kämäräinen, J.K.[Joni-Kristian], Huttunen, H.[Heikki],
Loop-closure detection by LiDAR scan re-identification,
ICPR21(9107-9114)
IEEE DOI 2105
Location awareness, Training, Measurement, Laser radar, Simultaneous localization and mapping, Navigation, Network architecture BibRef

Chng, C.K.[Chee-Kheng], Parra, A.[Alvaro], Chin, T.J.[Tat-Jun], Latif, Y.[Yasir],
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure,
DICTA20(1-8)
IEEE DOI 2201
Visualization, Tracking, Estimation, Cameras, Trajectory, Task analysis, Visual odometry BibRef

Zheng, J.J.[Jian-Jie], Zhang, H.T.[Hai-Tao], Kong, W.D.[Wei-Di], Tang, K.[Kai],
A SLAM Loop Closure Algorithm of BoW Incorporating the Gray Level of Pixel,
CVIDL20(360-363)
IEEE DOI 2102
image matching, mobile robots, pattern clustering, robot vision, SLAM (robots), trees (mathematics), Gray Level BibRef

Ryohei, Y., Kanji, T., Koji, T.,
Invariant Spatial Information for Loop-Closure Detection,
MVA19(1-6)
DOI Link 1911
feature extraction, mobile robots, robot vision, SLAM (robots), query/mapped image, invariant coordinate system, Task analysis BibRef

Tsintotas, K.A.[Konstantinos A.], Giannis, P.[Panagiotis], Bampis, L.[Loukas], Gasteratos, A.[Antonios],
Appearance-based Loop Closure Detection with Scale-restrictive Visual Features,
CVS19(75-87).
Springer DOI 1912
For autonomous robots. BibRef

Miraldo, P.[Pedro], Saha, S.[Surojit], Ramalingam, S.[Srikumar],
Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment,
CVPR19(9691-9700).
IEEE DOI 2002
BibRef

Fu, Z.H.[Zhi-Heng], Guo, Y.L.[Yu-Lan], An, W.[Wei],
Simultaneous Context Feature Learning and Hashing for Large Scale Loop Closure Detection,
ICPR18(1689-1694)
IEEE DOI 1812
Feature extraction, Visualization, Learning systems, Convolution, Dimensionality reduction, Training, Robots BibRef

Bellavia, F.[Fabio], Fanfani, M.[Marco],
Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment,
CIAP13(I:462-471).
Springer DOI 1311
BibRef

Patra, S., Aggarwal, H., Arora, H., Banerjee, S., Arora, C.,
Computing Egomotion with Local Loop Closures for Egocentric Videos,
WACV17(454-463)
IEEE DOI 1609
BibRef

Chen, X.Y.[Xie-Yuanli], Lu, H.M.[Hui-Min], Xiao, J.H.[Jun-Hao], Zhang, H.[Hui], Wang, P.[Pan],
Robust Relocalization Based on Active Loop Closure for Real-Time Monocular SLAM,
CVS17(131-143).
Springer DOI 1711
BibRef

Chen, J.B.[Jian-Bin], Li, J.[Jun], Xu, Y.[Yang], Shen, G.T.[Guang-Tian], Gao, Y.J.[Yang-Jian],
A compact loop closure detection based on spatial partitioning,
ICIVC17(371-375)
IEEE DOI 1708
Image segmentation, Robots, Visualization, BoW, K-mean, Loop closure detection, Scene, segmentation BibRef

Laskar, Z., Huttunen, S., Herrera, D., Rahtu, E., Kannala, J.,
Robust loop closures for scene reconstruction by combining odometry and visual correspondences,
ICIP16(2603-2607)
IEEE DOI 1610
Cameras BibRef

Fei, X.O.[Xia-Ohan], Tsotsos, K.[Konstantine], Soatto, S.[Stefano],
A Simple Hierarchical Pooling Data Structure for Loop Closure,
ECCV16(III: 321-337).
Springer DOI 1611
BibRef

Zeng, M.[Ming], Zheng, J.X.[Jia-Xiang], Cheng, X.[Xuan], Liu, X.G.[Xin-Guo],
Templateless Quasi-rigid Shape Modeling with Implicit Loop-Closure,
CVPR13(145-152)
IEEE DOI 1309
3d modeling; depth camera; human modeling; nonrigid deformation BibRef

Khan, S., Wollherr, D., Buss, M.,
PIRF 3D: Online spatial and appearance based loop closure,
ICARCV12(335-340).
IEEE DOI 1304
BibRef

Chapoulie, A.[Alexandre], Rives, P.[Patrick], Filliat, D.[David],
A spherical representation for efficient visual loop closing,
OMNIVIS11(335-342).
IEEE DOI 1201
Did the robot return to the starting point. BibRef

Pradeep, V.[Vivek], Medioni, G.[Gerard], Weiland, J.[James],
Visual loop closing using multi-resolution SIFT grids in metric-topological SLAM,
CVPR09(1438-1445).
IEEE DOI 0906
Simultaneous Localization and Mapping. Build 3-D model of the world to use in navigation. BibRef

Kumar, A.[Ankita], Tardif, J.P.[Jean-Philippe], Anati, R.[Roy], Daniilidis, K.[Kostas],
Experiments on visual loop closing using vocabulary trees,
VisLoc08(1-8).
IEEE DOI 0806
Initial from GPS. Long sequence in urban environment. BibRef

Triebel, R.[Rudolph], Pfaff, P., Burgard, W.[Wolfram],
Multi level surface maps for outdoor terrain mapping and loop closing,
IROS06(xx-yy). BibRef 0600

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Vision-Language Navigation .


Last update:Aug 14, 2022 at 21:20:19