Enhancing Object Mapping in SLAM using CNN
Automation is becoming more prevalent among manufacturing and eCommerce companies as a way to better serve their customers. One of the key problems in warehouse management is controlling the internal delivery/movement of goods/objects. It is labor-intensive, time-consuming, and needs additional care based on delicacy goods. Automated guided vehicles (AGVs) that are small in size can serve as a solution to the aforementioned problem of locomotion. For any robot to move autonomously, the initial and critical requirement is to understand the surrounding environment precisely. Simultaneous Localisation and Mapping (SLAM) is the preferred method to build an environment map at runtime. SLAM is designed to work in a static environment and faces a few challenges once it involves dynamic objects. This research proposes Deep Learning to enhance the SLAM technique. It aids the identification of static and dynamic objects and consequently updates the occupancy grid map. The proposed approach has been validated through a simulated environment and a Convolution Neural Network (CNN) for the classification of static and dynamic objects. The simulation results demonstrate the promising nature of the proposed approach.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Bailey, T. and Durrant-Whyte, H. 2006. Simultaneous Localization and Mapping (SLAM): Part ii. IEEE Robotics Automation magazine 13, 108–117. DOI: https://doi.org/10.1109/MRA.2006.1678144
- Biswas, R., Limketkai, B., Sanner, S., and Thrun, S. 2002. Towards object mapping in non-stationary environments with mobile robots. In IEEE/RSJ international conference on intelligent robots and systems. 1014–1019.
- Coumans, E. 2018. Bullet real-time physics simulation-home of bullet and pybullet: Physics simulation for games, visual effects, robotics and reinforcement learning-page 3. https://pybullet.org/wordpress/index.php/page/3/.
- Coumans, E. and Bai, Y. 2016. Pybullet quickstart guide. Google Documents. https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.2ye70wns7io3.
- Darms, M., Rybski, P., and Urmson, C. 2008. Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. In 2008 IEEE Intelligent Vehicles Symposium. 1197 – 1202. DOI: https://doi.org/10.1109/IVS.2008.4621259
- Durrant-Whyte, H. and Bailey, T. 2006. Simultaneous localization and mapping: Part i. IEEE Robotics Automation Magazine 13, 99–110. DOI: https://doi.org/10.1109/MRA.2006.1638022
- Goodfellow, I. J., Bengio, Y., and Courville, A. 2016. Deep Learning. MIT Press, Cambridge, MA, USA. http://www.deeplearningbook.org.
- Grisetti, G., K¨ummerle, R., Stachniss, C., and Burgard, W. 2010. A tutorial on graphbased slam. IEEE Transactions on Intelligent Transportation Systems Magazine 2, 31–43. DOI: https://doi.org/10.1109/MITS.2010.939925
- Guclu, O. and Can, A. B. 2019. Fast and effective loop closure detection to improve SLAM performance. Journal of Intelligent Robotic Systems 93, 495–517. DOI: https://doi.org/10.1007/s10846-017-0718-z
- Hahnel, D., Triebel, R., Burgard, W., and Thrun, S. 2003. Map building with mobile robots in dynamic environments. In 2003 IEEE International Conference on Robotics and Automation. 1557–1563.
- Iqbal, J., Xu, R., Halloran, H., and Li, C. 2020. Development of a multi-purpose autonomous differential drive mobile robot for plant phenotyping and soil sensing. Electronics 9, 1550. DOI: https://doi.org/10.3390/electronics9091550
- Joerger, M. and Pervan, B. 2018. Quantifying safety of laser-based navigation. IEEE Transactions on Aerospace and Electronic Systems 55, 273–288. DOI: https://doi.org/10.1109/TAES.2018.2850381
- Jung, M. and Song, J.-B. 2016. Graph slam for agv using geometrical arrangement based on lamp and surf features in a factory environment. In 2016 16th International Conference on Control, Automation and Systems (ICCAS). 844–848. DOI: https://doi.org/10.1109/ICCAS.2016.7832411
- Lin, K.-H. and Wang, C.-C. 2010. Stereo-based simultaneous localization, mapping and moving object tracking. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. 3975–3980.
- Liu, Z., Liu, J., Chen, W., Wu, X., and Li, Z. 2021. Faminet: Learning real-time semisupervised video object segmentation with steepest optimized optical flow. IEEE Transactions on Instrumentation and Measurement 71, 1–16. DOI: https://doi.org/10.1109/TIM.2021.3133003
- Mehami, J., Nawi, M., and Zhong, R. 2018. Smart automated guided vehicles for manufacturing in the context of industry 4.0. Procedia Manufacturing Vol.26, 1077–1086. DOI: https://doi.org/10.1016/j.promfg.2018.07.144
- Migliore, D., Rigamonti, R., Marzorati, D., Matteucci, M., Sorrenti, D. G., et al. 2009. Use a single camera for simultaneous localization and mapping with mobile object tracking in dynamic environments. In ICRA Workshop on Safe navigation in open and dynamic environments: Application to autonomous vehicles.
- Ravi Kiran, B., Rold˜ao, L., Irastorza, B., Verastegui, R., S¨uss, S., Yogamani, S., Talpaert, V., Lepoutre, A., and Trehard, G. 2018. Real-time dynamic object detection for autonomous driving using prior 3d-maps. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops. 567–582. DOI: https://doi.org/10.1007/978-3-030-11021-5_35
- Rogers, J. G., Trevor, A. J., Nieto-Granda, C., and Christensen, H. I. 2010. Slam with expectation maximization for moveable object tracking. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2077–2082. DOI: https://doi.org/10.1109/IROS.2010.5652091
- Shinde, C., Lima, R., and Das, D. K. 2019. Deep reinforcement learning based dynamic object detection and tracking from a moving platform. In 2019 Sixth Indian Control Conference (ICC). 244–249. DOI: https://doi.org/10.1109/ICC47138.2019.9123158
- Sotoodeh Bahraini, M., Rad, A., and Bozorg, M. 2019. Slam in dynamic environments: A deep learning approach for moving object tracking using ml-ransac algorithm. Sensors 19, 3699. DOI: https://doi.org/10.3390/s19173699
- Sucan, I. and Kay, J. Ros documentation- wiki. Vincent, J., Labb’e, M., Lauzon, J.-S., Grondin, F., Comtois-Rivet, P.-M., and Michaud, F. 2020. Dynamic object tracking and masking for visual slam. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4974–4979.
- Wang, C.-C., Thorpe, C., and Thrun, S. 2003. Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas. In 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422). 842–849.
- Wolf, D. and Sukhatme, G. 2005. Mobile robot simultaneous localization and mapping in dynamic environments. Auton. Robots 19, 53–65. DOI: https://doi.org/10.1007/s10514-005-0606-4
- Xie, W., Liu, P. X., and Zheng, M. 2021. Moving object segmentation and detection for robust rgbd-slam in dynamic environments. IEEE Transactions on Instrumentation and Measurement 70, 1–8. DOI: https://doi.org/10.1109/TIM.2020.3026803