Abstract
Map construction and path planning are two critical problems for an autonomous navigation system. One traditional map construction method is to construct a 2D grid map based on LiDAR, but this method has some limits. It easily ignores 3D information which affects the accuracy of navigation. Another one is visual SLAM techniques, such as ORB-SLAM2 and S-PTAM algorithms, which can recognize 3D objects. But the visual methods perform not well because of light changes. Some conventional path planning algorithms, such as TEB and DWA, are proposed for auto-navigation. However, those algorithms are likely to go to a stalemate due to local optimum, or have the problems of collision caused by sudden speed changes in constrained environments. In order to address these issues, this paper proposes a multi-sensor fusion method for map construction and autonomous navigation. Firstly, the fusion model combines RGB-D, lidar laser, and inertial measurement unit (IMU) to construct 2D grid maps and 3D color point cloud maps in real-time. Next, we present an improved local planning algorithm (Opt_TEB) to solve the velocity mutation problem, enabling the robot to get a collision-free path. We implemented the whole system based on the ROS framework, which is a wide used an open-source robot operating system. The map construction and path planning algorithms are running on the robot, while the visualization and control modules are deployed on a back-end server. The experimental results illustrate that the multi-sensor fusion algorithm is able to conform to the original map more than the 2D grid map. Furthermore, our improved algorithm Opt_TEB performs smoothly and has no collision with obstacles in 30 trials. The navigation speed is improved by 4.2% and 11.5% compared to TEB and DWA, respectively.
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References
Wang, P., Wang, Y., Wang, X., Liu, Y., Zhang, J.: An intelligent actuator of an indoor logistics system based on multi-sensor fusion. Actuators 10, 120 (2021)
Song, S., Baba, J., Nakanishi, J., Yoshikawa, Y., Ishiguro, H.: Teleoperated robot sells toothbrush in a shopping mall: a field study. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–6. Association for Computing Machinery, New York, NY, USA (2021)
Makris, S.: Synthesis of data from multiple sensors and wearables for human–robot collaboration. In: Cooperating Robots for Flexible Manufacturing. Springer Series in Advanced Manufacturing. pp. 321–-338. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51591-1_17
Zhang, X., Fang, Z., Lu, Z., Xiao, J., Cheng, X., Zhang, X.: 3D reconstruction of weak feature indoor scenes based on hector SLAM and floorplan generation. In: IEEE 7th International Conference on Virtual Reality, pp. 117–126. IEEE, China (2021)
Fan, X., Wang, Y., Zhang, Z.: An evaluation of Lidar-based 2D SLAM techniques with an exploration mode. J. Phys. 1905, 012021 (2021)
Liu, B., Guan, Z., Li, B., Wen, G., Zhao, Y.: Research on SLAM algorithm and navigation of mobile robot based on ROS. In: 2021 IEEE International Conference on Mechatronics and Automation, pp. 119–124. IEEE, Takamatsu (2021)
Diao, Y., Cen, R., Xue, F., Su, X.: ORB-SLAM2S: a fast ORB-SLAM2 system with sparse optical flow tracking. In: 13th International Conference on Advanced Computational Intelligence, pp. 160–165. IEEE, Wanzhou (2010)
Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Berlles, J.J.: S-ptam: stereo parallel tracking and mapping. Robot. Auton. Syst. 93, 27–42 (2017)
Babu, B. W., Kim, S., Yan, Z., Ren, L.: σ-dvo: sensor noise model meets dense visual odometry. In: 2016 IEEE International Symposium on Mixed and Augmented Reality, pp. 18–26 (2016)
Luo, M., Hou, X., Yang, J.: Surface optimal path planning using an extended Dijkstra algorithm. IEEE Access 8, 147827–147838 (2016)
Liu, Z., Liu, H., Lu, Z., Zeng, Q.: A dynamic fusion pathfinding algorithm using delaunay triangulation and improved A-Star for mobile robots. IEEE Access 9, 20602–20621 (2021)
Stentz, A.: Optimal and efficient path planning for partially known environments. In: Hebert, M.H., Thorpe, C., Stentz, A. (eds.) Intelligent Unmanned Ground Vehicles. The Springer International Series in Engineering and Computer Science, vol 388, pp. 203–220. Springer, Boston (1997). https://doi.org/10.1007/978-1-4615-6325-9_11
Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3-D mapping with an RGB-D camera. IEEE Trans. Robot. 30, 177–187 (2013)
Akir, E., Ulukan, Z., Acarman, T.: Hortest fuzzy hamiltonian cycle on transportation network using minimum vertex degree and time-dependent dijkstra's algorithm. In: 16th IFAC Symposium on Control in Transportation Systems CTS 2021, vol. 54, pp. 348–353. IFAC-PapersOnLine, Lille (2021)
Tang, G., Tang, C., Claramunt, C., Hu, X., Zhou, P.: Geometric A-star algorithm: an improved A-star Algorithm for AGV path planning in a port environment. IEEE Access 99, 1 (2021)
Newman, W.S.: A Systematic Approach to Learning Robot Programming with ROS, 1st edn. Chapman and Hall/CRC, New York (2017)
Crick, C., Jay, G., Osentoski, S., Pitzer, B., Jenkins, O.C.: Rosbridge: ROS for non-ROS users. In: Christensen, H.I., Khatib, O. (eds.) Robotics Research. STAR, vol. 100, pp. 493–504. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-29363-9_28
Rosmann, C., Feiten, W., Wosch, T., Hoffmann, F., Bertram, T.: Efficient trajectory optimization using a sparse model. In: 2013 European Conference on Mobile Robots, pp. 25–27. IEEE, Barcelona (2014)
Carlone, L., Aragues, R., Castellanos, J.A., Bona, B.: A linear approximation for graph-based simultaneous localization and mapping. Robot. Sci. Syst. VII (2014)
Chang, L., Shan, L., Jiang, C., Dai, Y.: Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment. Auton. Robots 33, 268–304 (2020)
Cho, J.H., Pae, D.S., Lim, M.T., Kang, T.K.: A real-time obstacle avoidance method for autonomous vehicles using an obstacle-dependent gaussian potential field. J. Adv. Transp. 2018, 1–15 (2018)
Bampis, K., Amanatiadis, A.: Fast loop-closure detection using visual-word-vectors from image sequences. Int. J. Robot. Res. 37, 62–82 (2018)
Li, S., Lee, D.: RGB-D SLAM in dynamic environments using static point weighting. IEEE Robot. Autom. Lett. 99, 2263–2270 (2017)
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This work was supported by National Natural Science Foundation of China (no. 61702320).
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Wang, H., Chen, N., Yang, D., Fan, G. (2022). Autonomous Navigation System for Indoor Mobile Robots Based on a Multi-sensor Fusion Technology. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_39
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DOI: https://doi.org/10.1007/978-981-19-4546-5_39
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