{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T02:10:09Z","timestamp":1736388609499,"version":"3.32.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Pose estimation methods for robotics should return a distribution of poses rather than just a single pose estimate. Motivated by this, in this work we investigate multi-modal pose representations for reliable 6-DoF object tracking. A neural network architecture for simultaneous object segmentation and estimation of fiducial points of the object on RGB images is proposed. Given a priori probability distribution of object poses a particle filter is employed to estimate the posterior probability distribution of object poses. An advanced observation model relying on matching the projected 3D model with the segmented object and a distance transform-based object representation is used to weight samples representing the probability distribution. Afterwards, the object pose determined by the PnP algorithm is included in the probability distribution via replacing a particle with the smallest weight. Next, a k-means++ algorithm is executed to determine modes in a multi-modal probability distribution. A multi-swarm particle swarm optimization is then executed to determine the finest modes in the probability distribution. A subset of particles for final pose optimization is found in a multi-criteria analysis using the TOPSIS algorithm. They are verified using conflicting criteria that are determined on the basis of object keypoints, segmented object, and the distance transform. On the challenging YCB-Video dataset it outperforms recent algorithms for both object pose estimation and object pose tracking.<\/jats:p>","DOI":"10.1007\/s10846-024-02181-5","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T02:01:41Z","timestamp":1728439301000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Modal Pose Representations for 6-DOF Object Tracking"],"prefix":"10.1007","volume":"110","author":[{"given":"Mateusz","family":"Majcher","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogdan","family":"Kwolek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"2181_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103898","volume":"96","author":"C Sahin","year":"2020","unstructured":"Sahin, C., Garcia-Hernando, G., Sock, J., Kim, T.K.: A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators. Image Vis. Comput. 96, 103898 (2020)","journal-title":"Image Vis. Comput."},{"key":"2181_CR2","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: \u201cPoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes.\u201d in IEEE\/RSJ Int. Conf. on Intel. Robots Syst. (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"issue":"2","key":"2181_CR3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10846-023-01957-5","volume":"109","author":"X Zhong","year":"2023","unstructured":"Zhong, X., Zhu, W., Liu, W., Yi, J., Liu, C., Wu, Z.: G-SAM: A robust one-shot keypoint detection framework for PnP based robot pose estimation. J. Intell. Robot. Syst. 109(2), 28 (2023)","journal-title":"J. Intell. Robot. Syst."},{"key":"2181_CR4","volume-title":"6D object position estimation from 2D images: a literature review","author":"G Marullo","year":"2022","unstructured":"Marullo, G., Tanzi, L., Piazzolla, P., Vezzetti, E.: 6D object position estimation from 2D images: a literature review. Multimed, Tools Appl (2022)"},{"key":"2181_CR5","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., Fua, P.: \u201cReal-time seamless single shot 6D object pose prediction.\u201d in IEEE\/CVF Conf. Comput. Vis. Patt. Recognit., pp. 292\u2013301. (2018)","DOI":"10.1109\/CVPR.2018.00038"},{"key":"2181_CR6","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhou, B., Russell, R., Kee, V., Wagner, S., Hebert, M., Torralba, A., Johnson, D.M.: \u201cReal-time object pose estimation with pose interpreter networks.\u201d in IEEE\/RSJ Int. Conf. Intell. Robot. Syst. (IROS), pp. 6798\u20136805. (2018)","DOI":"10.1109\/IROS.2018.8593662"},{"key":"2181_CR7","doi-asserted-by":"crossref","unstructured":"Zakharov, S., Shugurov, I., Ilic, S.: \u201cDPOD: 6D pose object detector and refiner.\u201d in IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), pp. 1941\u20131950. (2019)","DOI":"10.1109\/ICCV.2019.00203"},{"key":"2181_CR8","doi-asserted-by":"crossref","unstructured":"Fan, Z., Zhu, Y., He, Y., Sun, Q., Liu, H., He, J.: \u201cDeep learning on monocular object pose detection and tracking: A comprehensive overview.\u201dACM Comput. Surv. 55(4), (2022)","DOI":"10.1145\/3524496"},{"key":"2181_CR9","doi-asserted-by":"crossref","unstructured":"Tuscher, M., H\u00f6rz, J., Driess, D., Toussaint, M.: \u201cDeep 6-DoF tracking of unknown objects for reactive grasping.\u201d in IEEE Int. Conf. Robot. Autom. (ICRA), pp. 14185\u201314191. (2021)","DOI":"10.1109\/ICRA48506.2021.9561416"},{"key":"2181_CR10","doi-asserted-by":"crossref","unstructured":"Piga, N.A., Bottarel, F., Fantacci, C., Vezzani, G., Pattacini, U., Natale, L.: \u201cMaskUKF: An instance segmentation aided unscented Kalman filter for 6D object pose and velocity tracking.\u201d Front. Robot. AI, vol. 8, (2021)","DOI":"10.3389\/frobt.2021.594583"},{"key":"2181_CR11","unstructured":"Chidananda, P., Nair, S., Lee, D., Kaehler, A.: \u201cPixtrack: Precise 6DoF object pose tracking using NeRF templates and feature-metric alignment.\u201d (2022)"},{"issue":"12","key":"2181_CR12","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","volume":"22","author":"E Marchand","year":"2016","unstructured":"Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: A hands-on survey. IEEE Trans. on Vis. Comp. Graph. 22(12), 2633\u20132651 (2016)","journal-title":"IEEE Trans. on Vis. Comp. Graph."},{"issue":"2","key":"2181_CR13","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/TVCG.2017.2658570","volume":"24","author":"M Krichenbauer","year":"2018","unstructured":"Krichenbauer, M., Yamamoto, G., Taketom, T., Sandor, C., Kato, H.: Augmented reality versus virtual reality for 3D object manipulation. IEEE Trans. Vis. Comput. Graph. 24(2), 1038\u20131048 (2018)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"12","key":"2181_CR14","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.1109\/TVCG.2021.3089096","volume":"28","author":"K Thiel","year":"2022","unstructured":"Thiel, K., Naumann, F., Jundt, E., Gunnemann, S., Klinker, G.: C.dot - convolutional deep object tracker for augmented reality based purely on synthetic data. IEEE Trans. Vis. Comput. Graph. 28(12), 4434\u20134451 (2022)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"2181_CR15","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: \u201cPVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation.\u201d in IEEE Conf. CVPR, pp. 4556\u20134565. (2019)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"2181_CR16","doi-asserted-by":"crossref","unstructured":"Park, K., Patten, T., Vincze, M.: \u201cPix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation.\u201d in IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), pp. 7667\u20137676. (2019)","DOI":"10.1109\/ICCV.2019.00776"},{"key":"2181_CR17","doi-asserted-by":"crossref","unstructured":"Song, C., Song, J., Huang, Q.: \u201cHybridPose: 6D object pose estimation under hybrid representations.\u201d in IEEE\/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 428\u2013437. (2020)","DOI":"10.1109\/CVPR42600.2020.00051"},{"issue":"19","key":"2181_CR18","doi-asserted-by":"publisher","first-page":"12283","DOI":"10.1007\/s00521-020-05644-6","volume":"33","author":"SH Zabihifar","year":"2021","unstructured":"Zabihifar, S.H., Semochkin, A.N., Seliverstova, E.V., Efimov, A.R.: Unreal mask: one-shot multi-object class-based pose estimation for robotic manipulation using keypoints with a synthetic dataset. Neural Comput. Appl. 33(19), 12283\u201312300 (2021)","journal-title":"Neural Comput. Appl."},{"key":"2181_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118624","volume":"211","author":"H Tian","year":"2023","unstructured":"Tian, H., Song, K., Li, S., Ma, S., Xu, J., Yan, Y.: Data-driven robotic visual grasping detection for unknown objects: A problem-oriented review. Expert Syst. Appl. 211, 118624 (2023)","journal-title":"Expert Syst. Appl."},{"key":"2181_CR20","doi-asserted-by":"crossref","unstructured":"Morrison, D., Corke, P., Leitner, J.: \u201cClosing the loop for robotic grasping: Real-time, generative grasp synthesis approach.\u201d in Proc. Robot. Sci. Syst. (RSS), (2018)","DOI":"10.15607\/RSS.2018.XIV.021"},{"key":"2181_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103775","volume":"141","author":"TT Le","year":"2021","unstructured":"Le, T.T., Le, T.S., Chen, Y.R., Vidal, J., Lin, C.Y.: 6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping. Robot. Auton. Syst. 141, 103775 (2021)","journal-title":"Robot. Auton. Syst."},{"issue":"24","key":"2181_CR22","doi-asserted-by":"publisher","first-page":"7108","DOI":"10.1364\/AO.465168","volume":"61","author":"X Ge","year":"2022","unstructured":"Ge, X., Chi, S., Jia, W., Jiang, K.: Real-time pose estimation for an underwater object combined with deep learning and prior information. Appl. Opt. 61(24), 7108\u20137118 (2022)","journal-title":"Appl. Opt."},{"key":"2181_CR23","doi-asserted-by":"crossref","unstructured":"dos Santos J\u00fanior, J.G., Silva do Monte Lima, J.P., Teichrieb, V.: \u201cOcclusion-robust method for RGB-D 6-DOF object tracking with particle swarm optimization.\u201d Expert Syst. Appl., vol. 174, p. 114736. (2021)","DOI":"10.1016\/j.eswa.2021.114736"},{"key":"2181_CR24","doi-asserted-by":"crossref","unstructured":"Majcher, M., Kwolek, B.: \u201cDeep quaternion pose proposals for 6D object pose tracking.\u201d in IEEE\/CVF Int. Conf. Comput. Vis. Work. (ICCVW), pp. 243\u2013251. (2021)","DOI":"10.1109\/ICCVW54120.2021.00032"},{"key":"2181_CR25","doi-asserted-by":"crossref","unstructured":"Rozumnyi, D., Kotera, J., Sroubek, F., Novotny, L., Matas, J.: \u201cThe world of fast moving objects.\u201d in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4838\u20134846. (2017)","DOI":"10.1109\/CVPR.2017.514"},{"key":"2181_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, D., Barbot, A., Seichepine, F., Lo, F.P.W., Bai, W., Yang, G.Z., Lo, B.: \u201cMicro-object pose estimation with sim-to-real transfer learning using small dataset.\u201d Commun. Phys. 5(1), (2022)","DOI":"10.1038\/s42005-022-00844-z"},{"key":"2181_CR27","doi-asserted-by":"crossref","unstructured":"Deng, X., Mousavian, A., Xiang, Y., Xia, F., Bretl, T., Fox, D.: \u201cPoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking.\u201d in Robot. Sci. Syst. (RSS), (2019)","DOI":"10.15607\/RSS.2019.XV.049"},{"issue":"5","key":"2181_CR28","doi-asserted-by":"publisher","first-page":"2012","DOI":"10.1109\/TCST.2020.3026926","volume":"29","author":"A Sveier","year":"2021","unstructured":"Sveier, A., Egeland, O.: Dual quaternion particle filtering for pose estimation. IEEE Trans. Control Syst. Technol. 29(5), 2012\u20132025 (2021)","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"2181_CR29","doi-asserted-by":"crossref","unstructured":"Li, W., Naeem, W., Ji, W., Liu, J., Hao, W., Chen, L.: \u201cPose estimation based on a dual quaternion feedback particle filter.\u201d in Int. Conf. Robot. Autom. (ICRA). IEEE Press, pp. 3460\u20133466. (2022)","DOI":"10.1109\/ICRA46639.2022.9812437"},{"issue":"3","key":"2181_CR30","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s10846-017-0714-3","volume":"92","author":"S Rosa","year":"2018","unstructured":"Rosa, S., Toscana, G., Bona, B.: Q-PSO: Fast quaternion-based pose estimation from RGB-D images. J. Intell. Robot. Syst. 92(3), 465\u2013487 (2018)","journal-title":"J. Intell. Robot. Syst."},{"issue":"10","key":"2181_CR31","doi-asserted-by":"publisher","first-page":"10281","DOI":"10.1109\/TIE.2021.3121721","volume":"69","author":"X Xing","year":"2022","unstructured":"Xing, X., Guo, J., Nan, L., Gu, Q., Zhang, X., Yan, D.M.: Efficient MSPSO sampling for object detection and 6-D pose estimation in 3-D scenes. IEEE Trans. Ind. Electron. 69(10), 10281\u201310291 (2022)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"2181_CR32","doi-asserted-by":"crossref","unstructured":"Majcher, M., Kwolek, B.: \u201cFiducial Points-supported Object Pose Tracking on RGB Images via Particle Filtering with Heuristic Optimization.\u201d in 16th Int. Conf. Comput. Vis.Theory Appl. (VISAPP). SciTePress, pp. 919\u2013926. (2021)","DOI":"10.5220\/0010237109190926"},{"issue":"4","key":"2181_CR33","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.1109\/TASE.2020.3021119","volume":"18","author":"H Dong","year":"2021","unstructured":"Dong, H., Prasad, D.K., Chen, I.M.: Object pose estimation via pruned hough forest with combined split schemes for robotic grasp. IEEE Trans. Autom. Sci. Eng. 18(4), 1814\u20131821 (2021)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"8","key":"2181_CR34","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/0305-0548(93)90109-V","volume":"20","author":"CL Hwang","year":"1993","unstructured":"Hwang, C.L., Lai, Y.J., Liu, T.Y.: A new approach for multiple objective decision making. Comput. Oper. Res. 20(8), 889\u2013899 (1993)","journal-title":"Comput. Oper. Res."},{"key":"2181_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmachtheory.2020.103861","volume":"150","author":"A Cohen","year":"2020","unstructured":"Cohen, A., Shoham, M.: Hyper dual quaternions representation of rigid bodies kinematics. Mech. Mach. Theory 150, 103861 (2020)","journal-title":"Mech. Mach. Theory"},{"issue":"2","key":"2181_CR36","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TNNLS.2015.2440473","volume":"27","author":"D Xu","year":"2016","unstructured":"Xu, D., Xia, Y., Mandic, D.P.: Optimization in quaternion dynamic systems Gradient, Hessian, and learning algorithms. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 249\u2013261 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"2181_CR37","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1007\/s10489-016-0867-y","volume":"46","author":"TT Khuat","year":"2017","unstructured":"Khuat, T.T., Le, M.H.: A genetic algorithm with multi-parent crossover using quaternion representation for numerical function optimization. Appl. Intell. 46(4), 810\u2013826 (2017)","journal-title":"Appl. Intell."},{"key":"2181_CR38","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.sigpro.2014.11.007","volume":"113","author":"B Lei","year":"2015","unstructured":"Lei, B., Zhou, F., Tan, E.L., Ni, D., Lei, H., Chen, S., Wang, T.: Optimal and secure audio watermarking scheme based on self-adaptive Particle Swarm Optimization and quaternion wavelet transform. Signal Process. 113, 80\u201394 (2015)","journal-title":"Signal Process."},{"key":"2181_CR39","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cviu.2019.01.005","volume":"180","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Cao, Q.: Holistic and local patch framework for 6D object pose estimation in RGB-D images. Comput. Vis. Image Underst. 180, 59\u201373 (2019)","journal-title":"Comput. Vis. Image Underst."},{"key":"2181_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.108969","volume":"207","author":"P Hao","year":"2023","unstructured":"Hao, P., Karaku\u015f, O., Achim, A.: A hybrid particle-stochastic map filter. Signal Process. 207, 108969 (2023)","journal-title":"Signal Process."},{"key":"2181_CR41","doi-asserted-by":"crossref","unstructured":"Kutschireiter, A., Surace, C., Sprekeler, H., Pfister, J.P.: \u201cNonlinear bayesian filtering and learning: a neuronal dynamics for perception.\u201d Sci. Rep. 7(1), (2017)","DOI":"10.1038\/s41598-017-06519-y"},{"issue":"2","key":"2181_CR42","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s10846-021-01409-y","volume":"102","author":"N Pessanha Santos","year":"2021","unstructured":"Pessanha Santos, N., Lobo, V., Bernardino, A.: Unscented particle filters with refinement steps for UAV pose tracking. J. Intell. Robot. Syst. 102(2), 52 (2021)","journal-title":"J. Intell. Robot. Syst."},{"issue":"2","key":"2181_CR43","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"D Wang","year":"2018","unstructured":"Wang, D., Tan, D., Liu, L.: Particle Swarm Optimization algorithm: An overview. Soft Comput. 22(2), 387\u2013408 (2018)","journal-title":"Soft Comput."},{"key":"2181_CR44","doi-asserted-by":"crossref","unstructured":"Gao, Y., Du, W., Yan, G.: \u201cSelectively-informed particle swarm optimization.\u201d Sci. Rep. 5(1), (2015)","DOI":"10.1038\/srep09295"},{"key":"2181_CR45","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2020.06.027","volume":"540","author":"S Wang","year":"2020","unstructured":"Wang, S., Liu, G., Gao, M., Cao, S., Guo, A., Wang, J.: Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators. Inf. Sci. 540, 175\u2013201 (2020)","journal-title":"Inf. Sci."},{"key":"2181_CR46","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3934\/jimo.2018038","volume":"15","author":"Q Cheng","year":"2019","unstructured":"Cheng, Q., Han, X., Zhao, T., Yadavalli, S.: Improved Particle Swarm Optimization and neighborhood field optimization by introducing the re-sampling step of Particle Filter. J. Ind. Manag. Opt. 15, 177\u2013198 (2019)","journal-title":"J. Ind. Manag. Opt."},{"key":"2181_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14901-4","volume-title":"A scale adaptive generative target tracking method based on modified particle filter","author":"X Yuqi","year":"2023","unstructured":"Yuqi, X., Yongjun, W., Fan, Y.: A scale adaptive generative target tracking method based on modified particle filter. Multimed, Tools Appl (2023)"},{"issue":"3","key":"2181_CR48","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.ifacol.2019.06.013","volume":"52","author":"A Akca","year":"2019","unstructured":"Akca, A., Efe, M.O.: Multiple model Kalman and particle filters and applications: A survey. IFAC-PapersOnLine 52(3), 73\u201378 (2019)","journal-title":"IFAC-PapersOnLine"},{"key":"2181_CR49","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.jcp.2019.06.060","volume":"396","author":"M Pulido","year":"2019","unstructured":"Pulido, M., van Leeuwen, P.J.: Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter. J. Comp. Phys. 396, 400\u2013415 (2019)","journal-title":"J. Comp. Phys."},{"issue":"1","key":"2181_CR50","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1146\/annurev-statistics-031017-100232","volume":"5","author":"P Fearnhead","year":"2018","unstructured":"Fearnhead, P., K\u00fcnsch, H.R.: Particle filters and data assimilation. Ann. Rev. Stat. Appl. 5(1), 421\u2013449 (2018)","journal-title":"Ann. Rev. Stat. Appl."},{"issue":"1","key":"2181_CR51","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s10846-021-01377-3","volume":"102","author":"RA Medeiros","year":"2021","unstructured":"Medeiros, R.A., Pimentel, G.A., Garibotti, R.: An embedded quaternion-based Extended Kalman Filter pose estimation for six degrees of freedom systems. J. Intell. Robot. Syst. 102(1), 18 (2021)","journal-title":"J. Intell. Robot. Syst."},{"key":"2181_CR52","doi-asserted-by":"crossref","unstructured":"Yuqi, X., Yongjun, W., Fan, Y.: \u201cA scale adaptive generative target tracking method based on modified particle filter.\u201d Multimed. Tools Appl., (2023)","DOI":"10.1007\/s11042-023-14901-4"},{"key":"2181_CR53","doi-asserted-by":"crossref","unstructured":"Vishak, P., Sudheesh, P., Jayakumar, M.: \u201cA survey on nonlinear applications of modified particle filter.\u201d in Int. Conf. Wirel. Commun. Signal Process. Netw., pp. 1059\u20131063. (2017)","DOI":"10.1109\/WiSPNET.2017.8299924"},{"issue":"2","key":"2181_CR54","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1109\/LCSYS.2020.3005066","volume":"5","author":"K Li","year":"2021","unstructured":"Li, K., Pfaff, F., Hanebeck, U.D.: Unscented dual quaternion particle filter for SE(3) estimation. IEEE Control Syst. Lett. 5(2), 647\u2013652 (2021)","journal-title":"IEEE Control Syst. Lett."},{"key":"2181_CR55","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: \u201cParticle Swarm Optimization.\u201d in Proc. of IEEE Int. Conf. Neural Networks, pp. 1942\u20131948, IEEE Press (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"1","key":"2181_CR56","doi-asserted-by":"publisher","first-page":"157","DOI":"10.3390\/make1010010","volume":"1","author":"S Sengupta","year":"2019","unstructured":"Sengupta, S., Basak, S., Peters, R.A.: Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 1(1), 157\u2013191 (2019)","journal-title":"Mach. Learn. Knowl. Extr."},{"issue":"6","key":"2181_CR57","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM. 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM."},{"key":"2181_CR58","doi-asserted-by":"crossref","unstructured":"Lepetit, V., Pilet, J., Fua, P.: \u201cPoint matching as a classification problem for fast and robust object pose estimation.\u201d in CVPR, pp. 244\u2013250. (2004)","DOI":"10.1109\/CVPR.2004.1315170"},{"key":"2181_CR59","doi-asserted-by":"crossref","unstructured":"Vidal, J., Lin, C.Y., Marti, R.: \u201c6D pose estimation using an improved method based on point pair features.\u201d in Int. Conf. on Control Autom. Robot. (ICCAR), pp. 405\u2013409. (2018)","DOI":"10.1109\/ICCAR.2018.8384709"},{"key":"2181_CR60","doi-asserted-by":"crossref","unstructured":"Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: \u201cSSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again.\u201d in IEEE Int. Conf. Comput. Vis., pp. 1530\u20131538. (2017)","DOI":"10.1109\/ICCV.2017.169"},{"key":"2181_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106839","volume":"218","author":"P Yin","year":"2021","unstructured":"Yin, P., Ye, J., Lin, G., Wu, Q.: Graph neural network for 6D object pose estimation. Knowl-Based Syst. 218, 106839 (2021)","journal-title":"Knowl-Based Syst."},{"key":"2181_CR62","doi-asserted-by":"crossref","unstructured":"Rad, M., Lepetit, V.: \u201cBB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth.\u201d in IEEE Int. Conf. Comp. Vis., pp. 3848\u20133856. (2017)","DOI":"10.1109\/ICCV.2017.413"},{"key":"2181_CR63","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: \u201cModel based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes.\u201d in Asian Conf. Comp. Vis., pp. 548\u2013562, Springer, (2013)","DOI":"10.1007\/978-3-642-37331-2_42"},{"key":"2181_CR64","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: \u201cLearning 6D object pose estimation using 3D object coordinates.\u201d in ECCV, pp. 536\u2013551, Springer (2014)","DOI":"10.1007\/978-3-319-10605-2_35"},{"key":"2181_CR65","unstructured":"Arthur, D., Vassilvitskii, S.: \u201cK-means++: The Advantages of Careful Seeding.\u201d in Proc. ACM-SIAM Symp. Discrete Algorithm., pp. 1027\u20131035. (2007)"},{"key":"2181_CR66","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: \u201cPyramid scene parsing network.\u201d in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 6230\u20136239. (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"2181_CR67","doi-asserted-by":"crossref","unstructured":"Wu, P., Lee, Y., Tseng, H., Ho, H., Yang, M., Chien, S.: \u201cA benchmark dataset for 6DoF object pose tracking.\u201d in IEEE Int. Symp. Mixed Aug. Reality, pp. 186\u2013191. (2017)","DOI":"10.1109\/ISMAR-Adjunct.2017.62"},{"issue":"3","key":"2181_CR68","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s11263-011-0514-3","volume":"98","author":"VA Prisacariu","year":"2012","unstructured":"Prisacariu, V.A., Reid, I.D.: PWP3D: Real-Time Segmentation and Tracking of 3D Objects. Int. J. Comput. Vis. 98(3), 335\u2013354 (2012)","journal-title":"Int. J. Comput. Vis."},{"key":"2181_CR69","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Michel, F., Krull, A., Yang, M., Gumhold, S., Rother, C.: \u201cUncertainty-driven 6D pose estimation of objects and scenes from a single RGB image.\u201d in CVPR, pp. 3364\u20133372. (2016)","DOI":"10.1109\/CVPR.2016.366"},{"issue":"14","key":"2181_CR70","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1177\/0278364916669237","volume":"35","author":"T Whelan","year":"2016","unstructured":"Whelan, T., Salas-Moreno, R.F., Glocker, B., Davison, A.J., Leutenegger, S.: ElasticFusion. Int. J. Rob. Res. 35(14), 1697\u20131716 (2016)","journal-title":"Int. J. Rob. Res."},{"issue":"8","key":"2181_CR71","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1109\/TPAMI.2018.2884990","volume":"41","author":"H Tjaden","year":"2019","unstructured":"Tjaden, H., Schwanecke, U., Sch\u00f6mer, E., Cremers, D.: A region-based Gauss-Newton approach to real-time monocular multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1797\u20131812 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2181_CR72","doi-asserted-by":"crossref","unstructured":"Valenca, L., Silva, L., Chaves, T., Gomes, A., Figueiredo, L., Cossio, L., Tandel, S., Lima, J.P., Simoes, F., Teichrieb, V.: \u201cReal-time monocular 6DoF tracking of textureless objects using photometrically-enhanced edges.\u201d in 16th Int. Conf. Comput. Vis. Theory Appl. (VISAPP), (2021)","DOI":"10.5220\/0010348707630773"},{"key":"2181_CR73","doi-asserted-by":"crossref","unstructured":"Bugaev, B., Kryshchenko, A., Belov, R.: \u201cCombining 3D model contour energy and keypoints for object tracking.\u201d in ECCV, pp. 55\u201370, Springer (2018)","DOI":"10.1007\/978-3-030-01258-8_4"},{"key":"2181_CR74","unstructured":"Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S.: \u201cDeep Object Pose Estimation for Semantic Robotic Grasping of Household Objects.\u201d in CoRL, ser. Proceedings of Machine Learning Research, vol.\u00a087, pp. 306\u2013316. PMLR (2018)"},{"key":"2181_CR75","doi-asserted-by":"crossref","unstructured":"Oberweger, M., Rad, M., Lepetit, V.: \u201cMaking Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation.\u201d in Eur. Conf. Comput. Vis., pp. 125\u2013141, Springer (2018)","DOI":"10.1007\/978-3-030-01267-0_8"},{"key":"2181_CR76","doi-asserted-by":"crossref","unstructured":"Zappel, M., Bultmann, S., Behnke, and : \u201c6D Object Pose Estimation Using Keypoints and Part Affinity Fields.\u201d in RoboCup 2021: Robot World Cup XXIV. pp. 78\u201390, Springer, (2022)","DOI":"10.1007\/978-3-030-98682-7_7"},{"key":"2181_CR77","doi-asserted-by":"crossref","unstructured":"Wang, G., Manhardt, F., Tombari, F., Ji, X.: \u201cGDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation.\u201d in IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR). IEEE Comp. Soc., pp. 16606\u201316616. (2021)","DOI":"10.1109\/CVPR46437.2021.01634"},{"key":"2181_CR78","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s11263-019-01250-9","volume":"128","author":"Y Li","year":"2020","unstructured":"Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: Deep iterative matching for 6D pose estimation. Int. J. Comput. Vis. 128, 657\u2013678 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"2181_CR79","doi-asserted-by":"crossref","unstructured":"Lepetit, V., Moreno-Noguer, F., Fua, P.:\u201cEPnP: An accurate O(n) solution to the PnP problem.\u201d Int. J. Comput. Vis. 81(2), 155\u2013166 (2009)","DOI":"10.1007\/s11263-008-0152-6"}],"container-title":["Journal of Intelligent &amp; Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-024-02181-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-024-02181-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-024-02181-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T03:12:51Z","timestamp":1736305971000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-024-02181-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"references-count":79,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2181"],"URL":"https:\/\/doi.org\/10.1007\/s10846-024-02181-5","relation":{},"ISSN":["1573-0409"],"issn-type":[{"type":"electronic","value":"1573-0409"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"assertion":[{"value":"22 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no competing interests that could have influenced the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}},{"value":"The research team involved within this research confirm that no ethical approval is required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}}],"article-number":"149"}}