Simultaneous Localization and Mapping of a Mobile Robot With Stereo Camera Using ORB Features

[1] Ambrus, R., Claici, S., Wendt, A.: Automatic room segmentation from unstructured 3-d data of indoor environments. IEEE Robotics and Automation Letters 2, 749–756 (2017) Search in Google Scholar

[2] Ball, D., Heath, S., Wiles, J., Wyeth, G., Corke, P., Milford, M.: Openratslam: an open source brainbased slam system. Autonomous Robots 34, 1–28 (04 2013). doi: 10.1007/s10514-012-9317-9 Search in Google Scholar

[3] Banino, A., Barry, C., Uria, B., Blundell, C., Lillicrap, T., Mirowski, P., Pritzel, A., Chadwick, M.J., Degris, T., Modayil, J., Wayne, G., Soyer, H., Viola, F., Zhang, B., Goroshin, R., Rabinowitz, N.C., Pascanu, R., Beattie, C., Petersen, S., Sadik, A., Gaffney, S., King, H., Kavukcuoglu, K., Hassabis, D., Hadsell, R., Kumaran, D.: Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018) Search in Google Scholar

[4] Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I.D., Leonard, J.J.: Simultaneous localization and mapping: Present, future, and the robust-perception age. CoRR abs/1606.05830 (2016), http://arxiv.org/abs/1606.05830 Search in Google Scholar

[5] Campos, C., Elvira, R., Rodríguez, J.J.G., Montiel, J.M.M., Tardós, J.D.: Orb-slam3: An accurate open-source library for visual, visual-inertial and multi-map slam (2020) Search in Google Scholar

[6] Davison, A., Reid, I., Molton, N., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1052–1067 (2007) Search in Google Scholar

[7] Doherty, K., Baxter, D., Schneeweiss, E., Leonard, J.: Probabilistic data association via mixture models for robust semantic slam. 2020 IEEE International Conference on Robotics and Automation (ICRA) pp. 1098–1104 (2020) Search in Google Scholar

[8] Doherty, K., Fourie, D., Leonard, J.: Multimodal semantic slam with probabilistic data association. 2019 International Conference on Robotics and Automation (ICRA) pp. 2419–2425 (2019) Search in Google Scholar

[9] Finman, R., Paull, L., Leonard, J.: Toward object-based place recognition in dense rgb-d maps. 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015) Search in Google Scholar

[10] Gálvez-López, D., Tardós, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics 28, 1188–1197 (2012) Search in Google Scholar

[11] Høydal, Ã., Skytøen, E., Andersson, S., Moser, M.B., Moser, E.: Object-vector coding in the medial entorhinal cortex. Nature 568, 1–8 (04 2019). doi: 10.1038/s41586-019-1077-7 Search in Google Scholar

[12] Kiggundu, A., Weber, C., Wermter, S.: A compressing auto-encoder as a developmental model of grid cells (02 2017) Search in Google Scholar

[13] Lowry, S.M., Sünderhauf, N., Newman, P., Leonard, J.J., Cox, D.D., Corke, P.I., Milford, M.J.: Visual place recognition: A survey. IEEE Trans. Robotics 32(1), 1–19 (2016). doi: 10.1109/TRO.2015.2496823. Search in Google Scholar

[14] Masone, C., Caputo, B.: A survey on deep visual place recognition. IEEE Access 9, 19516–19547 (2021). doi: 10.1109/ACCESS.2021.3054937, Search in Google Scholar

[15] Montiel, J., Civera, J., Davison, A.: Unified inverse depth parametrization for monocular slam. In: Robotics: Science and Systems (2006) Search in Google Scholar

[16] Mur-Artal, R., Tardós, J.D.: Orb-slam2: An opensource slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics 33, 1255–1262 (2017) Search in Google Scholar

[17] Pillai, S., Leonard, J.J.: Self-supervised visual place recognition learning in mobile robots. CoRR abs/1905.04453 (2019), http://arxiv.org/abs/1905.04453 Search in Google Scholar

[18] Raoui, Y., Göller, M., Devy, M., Kerscher, T., Zöllner, J.M., Dillmann, R., Coustou, A.: Rfidbased topological and metrical self-localization in a structured environment. 2009 International Conference on Advanced Robotics pp. 1–6 (2009) Search in Google Scholar

[19] Raoui, Y., Weber, C., Wermter, S.: Neoslam: Neural object slam for loop closure and navigation. In: Artificial Neural Networks and Machine Learning - ICANN 2022 - 31th International Conference on Artifiicial Neural Networks, Bristol, England, September 6-9, 2022, Proceedings, Part II (2022) Search in Google Scholar

[20] Rolls, E., Stringer, S., Elliot, T.: Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning. Network: Computation in Neural Systems 17, 447–465 (2006) Search in Google Scholar

[21] Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision. pp. 2564–2571 (2011). doi: 10.1109/ICCV.2011.6126544 Search in Google Scholar

[22] Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., Rus, D.: Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping (2020) Search in Google Scholar

[23] Shan, T., Englot, B.J., Duarte, F., Ratti, C., Rus, D.: Robust place recognition using an imaging lidar. CoRR abs/2103.02111 (2021), https://arxiv.org/abs/2103.02111 Search in Google Scholar

[24] Tourani, S., Desai, D., Parihar, U.S., Garg, S., Sarvadevabhatla, R.K., Krishna, K.M.: Early bird: Loop closures from opposing viewpoints for perceptually-aliased indoor environments. CoRR abs/2010.01421 (2020), https://arxiv.org/abs/2010.01421 Search in Google Scholar

[25] Volkov, M., Rosman, G., Feldman, D., III, J.W.F., Rus, D.: Coresets for visual summarization with applications to loop closure. In: IEEE International Conference on Robotics and Automation, ICRA 2015, Seattle, WA, USA, 26-30 May, 2015. pp. 3638–3645. IEEE (2015). doi: 10.1109/ICRA.2015.7139704. Search in Google Scholar

[26] Xiao, L., Wang, J., Qiu, X., Rong, Z., Zou, X.: Dynamic-slam: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. Robotics Auton. Syst. 117, 1–16 (2019) Search in Google Scholar

[27] Yongbao, A., Ting, R., Xiao-qiang, Y., Jia-lin, H., Lei, F., Jianbin, L., Ming, L.: Visual slam in dynamic environments based on object detection. Defence Technology (2020) Search in Google Scholar

[28] Yu, F., Shang, J., Hu, Y., Milford, M.: Neuroslam: a brain-inspired slam system for 3d environments. Biological Cybernetics 113(5-6), 515–545 (December 2019). doi: 10.1007/s00422-019-00806-9, https://eprints.qut.edu.au/198104/ Search in Google Scholar

[29] Zhou, X., Weber, C., Wermter, S.: Robot localization and orientation detection based on place cells and head-direction cells. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) Artificial Neural Networks and Machine Learning - ICANN 2017 - 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14,2017, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10613, pp. 137–145. Springer (2017). doi: 10.1007/978-3-319-68600-4_17. Search in Google Scholar