Simultaneous localization and mapping of mobile robots with multi-sensor fusion

[1] Wei, Y., Zhang, H., Zhong, H., et al. (2022). Indoor Environment Mapping of Mobile Robot Based on Efficient DSM-PF Method. IEEE Sensors Journal, 22. Search in Google Scholar

[2] Trybala, P., John, A., Kohler, C. (2022). Towards a mine 3D dense mapping mobile robot: A system design and preliminary accuracy evaluation. Markscheidewesen, (1), 129. Search in Google Scholar

[3] Vdisch, N., Cattaneo, D., Burgard, W., et al. (2022). Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning. Search in Google Scholar

[4] JD, Tardós. (2022). Data Association in SLAM. Search in Google Scholar

[5] Mueggler, E., Rebecq, H., Gallego, G., et al. (2017). The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. The International journal of robotics research, 36(2), 142-149. Search in Google Scholar

[6] Parker, L. E. (2022). Handbook of Robotics Chapter 40: Multiple Mobile Robot Systems. Search in Google Scholar

[7] Smith, M. J., Srivastava, A. (2022). A Model Based Approach to Statistical, Multi-Modal Sensor Fusion. Search in Google Scholar

[8] Wang, S., Gou, G., Sui, H., et al. (2022) CDSFusion: Dense Semantic SLAM for Indoor Environment Using CPU Computing. Remote Sensing, 14. Search in Google Scholar

[9] Caron, F., Duflos, E., Pomorski, D., et al. (2017). GPS/IMU Data Fusion using multisensor Kalman filtering: Introduction of contextual aspects. Information Fusion, 7(2), 221-230. Search in Google Scholar

[10] Xu, Z., Xiao, S. (2021). Spatial semantic graph enhanced monocular SLAM System. Computer Animation and Virtual Worlds, (4). Search in Google Scholar

[11] Wen, S., Lv, X., Lam, H. K., et al. (2021) Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment. Industrial Robot, ahead-of-print (ahead-of-print). Search in Google Scholar

[12] Ding, T., Zhang, Y., Ma, G., et al. (2022). Trajectory tracking of redundantly actuated mobile robot by MPC velocity control under steering strategy constraint Mechatronics, 84:102779-. Search in Google Scholar

[13] Lazkano, E., Astigarraga, A., Sierra, B. (2022). On the Adequateness of Emergency Exit Panel and Corridor Identification as Pilot Scheme for a Mobile Robot. Search in Google Scholar

[14] Rebecq, Henri, Mueggler, et al. (2017). The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM. International Journal of Robotics Research. Search in Google Scholar

[15] Wang, B., Nersesov, S., (2022). Ashrafiuon H. Formation Regulation and Tracking Control for Nonholonomic Mobile Robot Networks Using Polar Coordinates. IEEE Control Systems Letters, (6-). Search in Google Scholar

[16] Evita, M., Mustikawati, S. T., Djamal, M. (2022). Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application. IOP Publishing Ltd. Search in Google Scholar

[17] Cullen, J., Rasheed, A., Shamma, A. A., et al. (2022). Multi Sensor Fusion for On Line Monitoring of the Quality of Spot Welding in Automotive Industry. Search in Google Scholar

[18] Gao, N., & Fu, L. (2022). Study on the fusion of oil painting art and digital media based on a visual sensor. Journal of Sensors. Search in Google Scholar

[19] Mur-Artal, R., JD, Tardós. (2017). ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Transactions on Robotics. Search in Google Scholar

[20] Xiang, G., Tao, Z. (2017). Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Autonomous Robots. Search in Google Scholar