2D-3D hybrid mapping for path planning in autonomous robots
Tóm tắt
Computational complexity is one of the critical attributes of robot design. Mapping, a vitally important feature of auto-navigation of robots is one such area where computational complexity is of concern. An appropriate spatial representation of the surroundings is required for efficient path planning. Creation of a 2D map alone for a given environment is not sufficient even though it has the least computational complexity. 3D occupancy grid maps and 3D normal distribution maps need larger space and time. This research proposes an efficient hybrid mapping technique (2D-3Dh) which uses both 2D and 3D sensor data mapping to represent an efficient and compact representation of the environments aiming at reducing the computational complexity. The 2D maps are built from the laser data obtained from the Light Detection and Ranging (LiDAR) sensor and 3D maps are built from the data obtained from the Kinect sensor. The proposed algorithm efficiently organizes the 2D-3Dh map in such a way that the mapping switches from 2 to 3D only when an elevation region is detected in the environment. The newly generated map consists of both 2D and 3D data, with optimized mapping time and storage capacity requirements. Simulation results indicate that the proposed hybrid mapping can save mapping time up to 46.7% and storage space up to 98.5%. Real time experiments show that 2D-3Dh mapping can save more than 33% of memory space and requires less than half the time compared to that of full 3D mapping.
Tài liệu tham khảo
Barbosa, F.S., Duberg, D., Jensfelt, P., Tumova, J.: Guiding autonomous exploration with signal temporal logic. IEEE Robot. Autom. Lett. 4(4), 3332–3339 (2019). https://doi.org/10.1109/LRA.2019.2926669
Bosse, M., Zlot, R., Flick, P.: Zebedee: design of a spring-mounted 3-D Range sensor with application to mobile mapping. IEEE Trans. Robot. 28(5), 1104–1119 (2012). https://doi.org/10.1109/TRO.2012.2200990
Chen, L., He, Y., Chen, J., Li, Q., Zou, Q.: Transforming a 3-D LiDAR point cloud into a 2-D dense depth map through a parameter self-adaptive framework. IEEE Trans. Intell. Transp. Syst. 18(1), 165–176 (2017). https://doi.org/10.1109/TITS.2016.2564640
Chen, L., Peng, C.: A robust 2D-SLAM technology with environmental variation adaptability. IEEE Sens. J. 19(23), 11475–11491 (2019). https://doi.org/10.1109/JSEN.2019.2931368
Cui, X., et al.: 3D semantic map construction using improved orb-slam2 for mobile robot in edge computing environment. IEEE Access. (2020). https://doi.org/10.1109/ACCESS.2020.2983488
Dong, J., et al.: ViNav: a vision-based indoor navigation system for smartphones. IEEE Trans. Mob. Comput. 18(6) (2019)
Duberg, D., Jensfelt, P.: UFOMap: an efficient probabilistic 3D mapping framework that embraces the unknown. IEEE Robot. Autom. Lett. 5(4), 6411–6418 (2020). https://doi.org/10.1109/LRA.2020.3013861
Faireld, N., Kantor, G., Wettergreen, D.: `Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J. Field Robot. 24(1–2), 3–21 (2007)
Fengli, Y., Ju, L., Yannan, R., Jiande, S., Yuling, G., Wei, L.: Depth generation method for 2D to 3D conversion, in 2011 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Antalya, Turkey, 2011, pp. 1–4, https://doi.org/10.1109/3DTV.2011.5877196
Fraundorfer, F., et al.: Vision-based autonomous mapping and exploration using a quadrotor MAV,' in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Oct. 2012, pp. 4557_4564
Gutmann, J.-S., Fukuchi, M., Fujita, M.: 3D perception and environment map generation for humanoid robot navigation. Int. J. Robot. Res. 27(10), 1117–1134 (2008). https://doi.org/10.1177/0278364908096316
Haddeler, G., Aybakan, A., Akay, M.C., et al.: Evaluation of 3D LiDAR sensor setup for heterogeneous robot team. J Intell Robot Syst 100, 689–709 (2020). https://doi.org/10.1007/s10846-020-01207-y
Hornung, A., Wurm, K.M., Bennewitz, M., et al.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton Robot 34, 189–206 (2013). https://doi.org/10.1007/s10514-012-9321-0
Hähnel, D., Burgard, W., Thrun, S.: Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robot. Auton. Syst. 44(1), 15–27 (2003)
Han, J., et al.: Precise localization and mapping in indoor parking structures via parameterized SLAM. IEEE Trans. Intell. Transp. Syst. 20(12) (2019)
Javanmardi, M., Gu, Y., Javanmardi, E., Hsu, L., Kamijo, S.: 3D building map reconstruction in dense urban areas by integrating airborne laser point cloud with 2D boundary map, in 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Yokohama, Japan, 2015, pp. 126–131, https://doi.org/10.1109/ICVES.2015.7396906.
Kachurka, V., et al.: WeCo-SLAM: Wearable cooperative slam system for real-time indoor localization under challenging conditions. IEEE Sens. J. 22(6) (2022)
Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008). https://doi.org/10.1109/TRO.2008.2006706
Meagher, D.: Geometric modeling using octree encoding. Comput. Graph. Image Process. 19(2), 129–147 (1982)
Megalingam, R.K., Teja, C.R., Sreekanth, S., Raj, A.: ROS based autonomous indoor navigation simulation using SLAM algorithm. Int. J. Pure Appl. Math. 118, 199–205 (2018)
Megalingam, R.K., Rajendran, A.P., Dileepkumar, D., Soloman, A.T.: LARN: Indoor navigation for elderly and physically challenged, 2013 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, USA, 2013, pp. 326-330, https://doi.org/10.1109/GHTC.2013.6713705
Megalingam, R.K., Rajendraprasad, A., Manoharan, S.K.: Comparison of Planned Path and Travelled Path Using ROS Navigation Stack, in 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1–6, 10.1109/INCET49848.2020.9154132
Meng, Z., et al.: A two-stage optimized next-view planning framework for 3-D unknown environment exploration, and structural reconstruction. IEEE Robot. Autom. Lett. 2(3), 1680–1687 (2017)
Nie, F., Zhang, W., Yao, Z., Shi, Y., Li, F., Huang, Q.: LCPF: a particle filter lidar SLAM system with loop detection and correction. IEEE Access 8, 20401–20412 (2020). https://doi.org/10.1109/ACCESS.2020.2968353
Rajesh, K.M., Anandu, R., Akhil, R., Dhananjay, R., Chinta, R.T., Sarath, S., Ravi, S.: Self-E: a self-driving wheelchair for elders and physically challenged. Int. J. Intell. Robot. Appl. 5, 477–493 (2021). https://doi.org/10.1007/s41315-021-00209-9
Ren, R., Fu, H., Wu, M.: Large-scale outdoor SLAM based on 2D LiDAR. Electronics 8(6), 613–632 (2019)
Ryde, J., Hu, H.: 3D mapping with multi-resolution occupied voxel lists. Autom Robot 28, 169 (2010). https://doi.org/10.1007/s10514-009-9158-3
Saarinen, J.P., Andreasson, H., Stoyanov, T., Lilienthal, A.J.: 3D normal distributions transform occupancy maps: an effcient representation for mapping in dynamic environments. Int. J. Robot. Res. 32(14), 1627–1644 (2013)
Tang, S., et al.: A vertex-to-edge weighted closed-form method for dense rgb-d indoor slam. IEEE Access. (2019). https://doi.org/10.1109/ACCESS.2019.2900990
Tao, C., et al.: Indoor 3D semantic robot vslam based on mask regional convolutional neural network. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.2981648
Yang, S., Yang, S., Yi, X.: An efficient spatial representation for path planning of ground robots in 3D environments. IEEE Access 6, 41539–41550 (2018). https://doi.org/10.1109/ACCESS.2018.2858809
Yu, Y., Chen, R., et al.: Precise 3-D indoor localization based on wi-fi ftm and built-in sensors. IEEE Internet Things J. 7(12) (2020)
Yu, Y., et al.: A novel 3-D indoor localization algorithm based on ble and multiple sensors. IEEE Internet Things J. 8(11) (2021)
Yu, Y., et al.: Precise 3D indoor localization and trajectory optimization based on sparse wi-fi ftm anchors and built-in sensors. IEEE Trans. Veh. Technol. 71(4) (2022)
Zhang, H., Chen, N., Fan, G., Yang, D.: An improved scan matching algorithm in SLAM, in 2019 6th International Conference on Systems and Informatics (ICSAI), 2019, pp. 160–164. https://doi.org/10.1109/ICSAI48974.2019.9010259.
Zhao, J., Zhao, L., Huang, S., Wang, Y.: 2D laser SLAM with general features represented by implicit functions. IEEE Robot. Autom. Lett. 5(3), 4329–4336 (2020). https://doi.org/10.1109/LRA.2020.2996795