Object-aware data association for the semantically constrained visual SLAM (original) (raw)
References
Klein G, Murray D (2007) Parallel tracking and mapping for small ar workspaces. In: 2007 6th IEEE and ACM international symposium on mixed and augmented reality, IEEE, pp 225–234
Mur-Artal R, Tardos JD (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Robot 33(5):1255–1262 Article Google Scholar
Engel J, Sch¨o ps T, Cremers D (2014) Lsd-slam: Large-scale direct monocular slam. In: European conference on computer vision, Springer, pp 834–849
Engel J, Koltun V, Cremers D (2017) Direct sparse odometry. IEEE Trans Pattern Analy Mach Intell 40(3):611–625 Article Google Scholar
Lianos K-N, Schonberger JL, Pollefeys M, Sattler T (2018) Vso: visual semantic odometry. In: Proceedings of the European conference on computer vision (ECCV), pp 234–250
Yang S, Scherer S (2019) Cubeslam: monocular 3-d object slam. IEEE Trans Rob 35(4):925–938 Article Google Scholar
Iqbal A, Gans NR (2018) Localization of classified objects in slam using nonparametric statistics and clustering. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 161–168
Wu Y, Zhang Y, Zhu D, Feng Y, Coleman S, Kerr D (2020) Eao-slam: monocular semi-dense object slam based on ensemble data association. In: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 4966–4973
Nicholson L, Milford M, Sunderhauf N (2018) Quadricslam: dual quadrics from object detections as landmarks in object-oriented slam. IEEE Robot Autom Lett 4(1):1–8 Article Google Scholar
Campos C, Elvira R, Rodríguez JJG, Montiel JM, Tardόs JD (2021) Orb-slam3: an accurate open-source library for visual, visual-inertial, and multimap slam. IEEE Trans Robot 37(6):1874–1890 Article Google Scholar
Salas-Moreno RF, Glocken B, Kelly PH, Davison AJ (2014) Dense planar slam. In: 2014 IEEE international symposium on mixed and augmented reality (ISMAR), IEEE pp 157–164
Hsiao M, Westman E, Zhang G, Kaess M (2017) Keyframe-based dense planar slam. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 5110–5117.
Maity S, Saha A, Bhowmick B (2017) Edge slam: edge points based monocular visual slam. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2408–2417
Gomez-Ojeda R, Moreno F-A, Zuniga-Noel D, Scaramuzza D, Gonzalez-Jimenez J (2019) Pl-slam: a stereo slam system through the combination of points and line segments. IEEE Transactions on Robotics 35(3):734–746 Article Google Scholar
Pumarola A, Vakhitov A, Agudo A, Sanfeliu A, Moreno-Noguer, F (2017) Pl-slam: realtime monocular visual slam with points and lines. In: 2017 IEEE International conference on robotics and automation (ICRA), IEEE, pp 4503–4508
Zhou H, Zou D, Pei L, Ying R, Liu P, Yu W (2015) Structslam: Visual slam with building structure lines. IEEE Trans Vehic Technol 64(4):1364–1375 Article Google Scholar
DeTone D, Malisiewicz T, Rabinovich A (2018) Superpoint: self-supervised interest point detection and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 224–236
Dusmanu M, Rocco I, Pajdla T, Pollefeys M, Sivic J, Torii A, Sattler T (2019) D2-net: a trainable cnn for joint description and detection of local features. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8092–8101
Sarlin P-E, DeTone D, Malisiewicz T, Rabinovich A (2020) Superglue: learning feature matching with graph neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4938–4947
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495 Article Google Scholar
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848 Article Google Scholar
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:1245 Google Scholar
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C. (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, Springer, pp 21–37
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
He K, Gkioxari G, Doll´ar P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Bolya D, Zhou C, Xiao F, Lee YJ (2019) Yolact: real-time instance segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9157–9166
Kirillov A, He K, Girshick R, Rother C, Doll´ar P (2019) Panoptic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9404–9413
Bista SR, Hall D, Talbot B, Zhang H, Dayoub F, Su¨nderhauf N (2021) Evaluating the impact of semantic segmentation and pose estimation on dense semantic slam. In: 2021 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 5328–5335
Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP), IEEE pp 3464–3468
Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE, pp 3645–3649
Bowman SL, Atanasov N, Daniilidis K, Pappas GJ (2017) Probabilistic data association for semantic slam. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 1722–1729
Zhang L, Wei L, Shen P, Wei W, Zhu G, Song J (2018) Semantic slam based on object detection and improved octomap. IEEE Access 6:75545–75559 Article Google Scholar
Zhang J, Gui M, Wang Q, Liu R, Xu J, Chen S (2019) Hierarchical topic model based object association for semantic slam. IEEE Trans Visual Comput Graph 25(11):3052–3062 Article Google Scholar
Qian Z, Patath K, Fu J, Xiao J (2021) Semantic slam with autonomous object-level data association. In: 2021 IEEE international conference on robotics and automation (ICRA), IEEE, pp 11203–11209
Galvez-Lopez D, Tardos JD (2012) Bags of binary words for fast place recognition in image sequences. IEEE Trans Robot 28(5):188–1197 Article Google Scholar
Hermans A, Floros G, Leibe B (2014) Dense 3d semantic mapping of indoor scenes from rgb-d images. In: 2014 IEEE international conference on robotics and automation (ICRA), IEEE pp 2631–2638
McCormac J, Handa A, Davison A, Leutenegger S (2017) Semanticfusion: dense 3d semantic mapping with convolutional neural networks. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 4628–4635
Zhong F, Wang S, Zhang Z, Wang Y (2018) Detect-slam: making object detection and slam mutually beneficial. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1001–1010
Bescos B, F’acil JM, Civera J, Neira J (2018) Dynaslam: tracking, mapping, and inpainting in dynamic scenes. IEEE Robot Autom Lett 3(4):4076–4083 Article Google Scholar
Yu, C., Liu, Z., Liu, X.-J., Xie, F., Yang, Y., Wei, Q., Fei, Q.: Ds-slam: A semantic visual slam towards dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1168–1174 (2018). IEEE
Wang K, Lin Y, Wang L, Han L, Hua M, Wang X, Lian S, Huang B (2019) A unified framework for mutual improvement of slam and semantic segmentation. In: 2019 International conference on robotics and automation (ICRA), IEEE, pp 5224–5230
Ru¨nz M, Agapito L (2017) Co-fusion: Real-time segmentation, tracking and fusion of multiple objects. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 4471–4478
Runz M, Buffier M, Agapito L (2018) Maskfusion: real-time recognition, tracking and reconstruction of multiple moving objects. In: 2018 IEEE international symposium on mixed and augmented reality (ISMAR), IEEE, pp 10–20
Xu B, Li W, Tzoumanikas D, Bloesch M, Davison A, Leutenegger S (2019) Mid-fusion octree-based object-level multi-instance dynamic slam. In: 2019 International conference on robotics and automation (ICRA), IEEE, pp 5231–5237
Li P, Qin T, et al (2018) Stereo vision-based semantic 3d object and ego-motion tracking for autonomous driving. In: Proceedings of the European conference on computer vision (ECCV), pp 646–661
Henein M, Zhang J, Mahony R, Ila V (2020) Dynamic slam: the need for speed. In: 2020 IEEE international conference on robotics and automation (ICRA), IEEE, pp 2123–2129
Huang J, Yang S, Mu T-J, Hu S-M (2020) Clustervo: clustering moving instances and estimating visual odometry for self and surroundings. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2168–2177
Bescos B, Campos C, Tardo’s JD, Neira J (2021) Dynaslam ii: tightly-coupled multi-object tracking and slam. Robot Autom Lett 6(3):5191–5198 Article Google Scholar
Salas-Moreno RF, Newcombe RA, Strasdat H, Kelly PH, Davison AJ (2013) Slam++: simultaneous localisation and mapping at the level of objects. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1352–1359
Stenborg E, Toft C, Hammarstrand L (2018) Long-term visual localization using semantically segmented images. In: 2018 IEEE international conference on robotics and automation (ICRA), IEEE pp 6484–6490
Rublee E, Rabaud V, Konolige K, Bradski G (2011) Orb: an efficient alternative to sift or surf. In: 2011 International conference on computer vision, IEEE, pp 2564–2571
Ester M, Kriegel H-P, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol 96, pp 226–231
Jessee E, Wiebe E (2008) Visual perception and the hsv color system: Exploring color in the communications technology classroom. Technol Eng Teacher 68(1):7 Google Scholar
Vadivel A, Sural S, Majumdar AK (2005) Human color perception in the hsv space and its application in histogram generation for image retrieval. In: Color imaging X: processing, hardcopy, and applications, vol 5667. SPIE, pp 598–609
Sturm J, Engelhard N, Endres F, Burgard W, Cremers D (2012) A benchmark for the evaluation of rgb-d slam systems. In: Proceedings of the international conference on intelligent robot systems (IROS)
Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on computer vision and pattern recognition (CVPR)